Title: | Sensitivity Analysis for Comparative Methods |
---|---|
Description: | An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error). |
Authors: | Gustavo Paterno [cre, aut], Gijsbert Werner [aut], Caterina Penone [aut], Pablo Martinez [ctb] |
Maintainer: | Gustavo Paterno <[email protected]> |
License: | GPL-2 |
Version: | 0.8.5 |
Built: | 2024-10-27 03:19:59 UTC |
Source: | https://github.com/paternogbc/sensiPhy |
A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(alien)
data(alien)
A data frame with 94 rows and 7 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
gestaLen: Mean gestation length (days)
homeRange: Mean home range (km)
SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)
SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)
SE_range: Standard deviation (intraspecific) for mean home range (km)
A multiphylo containing 101 trees for 94 mammal species.
Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105
Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(alien)
data(alien)
A data frame with 94 rows and 7 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
gestaLen: Mean gestation length (days)
homeRange: Mean home range (km)
SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)
SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)
SE_range: Standard deviation (intraspecific) for mean home range (km)
A multiphylo containing 101 trees for 94 mammal species.
Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105
Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(alien)
data(alien)
A data frame with 94 rows and 7 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
gestaLen: Mean gestation length (days)
homeRange: Mean home range (km)
SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)
SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)
SE_range: Standard deviation (intraspecific) for mean home range (km)
A multiphylo containing 101 trees for 94 mammal species.
Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105
Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
Fits models for trait evolution of continuous characters, detecting influential clades
clade_continuous( data, phy, model, trait.col, clade.col, n.species = 5, n.sim = 20, bounds = list(), n.cores = NULL, track = TRUE, ... )
clade_continuous( data, phy, model, trait.col, clade.col, n.species = 5, n.sim = 20, bounds = list(), n.cores = NULL, track = TRUE, ... )
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
model |
The evolutionary model (see Details). |
trait.col |
The column in the provided data frame which specifies the trait to analyse (which should be a factor with two level) |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
bounds |
settings to constrain parameter estimates. See |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time,
fits a model of continuous character evolution using fitContinuous
,
repeats this many times (controlled by n.sim
), stores the results and calculates
the effects on model parameters Currently, only binary continuous traits are supported.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade.The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
Different evolutionary models from fitContinuous
can be used, i.e. BM
,OU
,
EB
, trend
, lambda
, kappa
, delta
and drift
.
See fitContinuous
for more details on evolutionary models.
Output can be visualised using sensi_plot
.
The function tree_continuous
returns a list with the following
components:
call
: The function call
data
: The original full data frame.
full.model.estimates
: Parameter estimates (rate of evolution sigsq
and where applicable optpar
), root state z0
,
AICc for the full model without deleted clades.
sensi.estimates
: Parameter estimates (sigsq and optpar), (percentual) difference
in parameter estimate compared to the full model (DIFsigsq, sigsq.perc, DIFoptpar, optpar.perc),
AICc and z0 for each repeat with a clade removed.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
errors
: Clades where deletion resulted in errors.
clade.col
: Which column was used to specify the clades?
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: data("primates") #Model trait evolution accounting for phylogenetic uncertainty clade_cont<-clade_continuous(data=primates$data,phy = primates$phy[[1]], model="OU", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=10,n.cores = 2,track=TRUE) #Print summary statistics summary(clade_cont) sensi_plot(clade_cont,graph="all") sensi_plot(clade_cont,clade="Cercopithecidae",graph = "sigsq") sensi_plot(clade_cont,clade="Cercopithecidae",graph = "optpar") #Change the evolutionary model, tree transformation or minimum number of species per clade clade_cont2<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="delta", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE) summary(clade_cont2) sensi_plot(clade_cont2) clade_cont3<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="BM", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE) summary(clade_cont3) sensi_plot(clade_cont3,graph="sigsq") ## End(Not run)
## Not run: data("primates") #Model trait evolution accounting for phylogenetic uncertainty clade_cont<-clade_continuous(data=primates$data,phy = primates$phy[[1]], model="OU", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=10,n.cores = 2,track=TRUE) #Print summary statistics summary(clade_cont) sensi_plot(clade_cont,graph="all") sensi_plot(clade_cont,clade="Cercopithecidae",graph = "sigsq") sensi_plot(clade_cont,clade="Cercopithecidae",graph = "optpar") #Change the evolutionary model, tree transformation or minimum number of species per clade clade_cont2<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="delta", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE) summary(clade_cont2) sensi_plot(clade_cont2) clade_cont3<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="BM", trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE) summary(clade_cont3) sensi_plot(clade_cont3,graph="sigsq") ## End(Not run)
Fits models for trait evolution of discrete (binary) characters, detecting influential clades
clade_discrete( data, phy, model, transform = "none", trait.col, clade.col, n.species = 5, n.sim = 20, bounds = list(), n.cores = NULL, track = TRUE, ... )
clade_discrete( data, phy, model, transform = "none", trait.col, clade.col, n.species = 5, n.sim = 20, bounds = list(), n.cores = NULL, track = TRUE, ... )
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
model |
The Mkn model to use (see Details). |
transform |
The evolutionary model to transform the tree (see Details). Default is |
trait.col |
The column in the provided data frame which specifies the trait to analyse (which should be a factor with two level) |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
bounds |
settings to constrain parameter estimates. See |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time,
fits a model of discrete character evolution using fitDiscrete
,
repeats this this many times (controlled by n.sim
), stores the results and calculates
the effects on model parameters. Currently, only binary discrete traits are supported.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade.The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
Different character model from fitDiscrete
can be used, including ER
(equal-rates),
SYM
(symmetric), ARD
(all-rates-different) and meristic
(stepwise fashion).
All transformations to the phylogenetic tree from fitDiscrete
can be used, i.e. none
,
EB
, lambda
, kappa
anddelta
.
See fitDiscrete
for more details on character models and tree transformations.
Output can be visualised using sensi_plot
.
The function tree_discrete
returns a list with the following
components:
call
: The function call
data
: The original full data frame.
full.model.estimates
: Parameter estimates (transition rates q12 and q21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for the full model without deleted clades.
sensi.estimates
: Parameter estimates (transition rates q12 and q21),(percentual) difference
in parameter estimate compared to the full model (DIFq12, sigsq.q12, DIFq21, optpar.q21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for each repeat with a clade removed.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
errors
: Clades where deletion resulted in errors.
clade.col
: Which column was used to specify the clades?
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: #Load data: data("primates") #Create a binary trait factor primates$data$adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") clade_disc<-clade_discrete(data=primates$data,phy = primates$phy[[1]],model="SYM", trait.col = "adultMass_binary",clade.col="family",n.sim=30,n.species=10,n.cores = 2) summary(clade_disc) sensi_plot(clade_disc) sensi_plot(clade_disc, clade = "Cebidae", graph = "q12") #Change the evolutionary model, tree transformation or minimum number of species per clade clade_disc_2<-clade_discrete(data=primates$data,phy = primates$phy[[1]], model="ARD",transform="kappa", trait.col = "adultMass_binary",clade.col="family",n.sim=30, n.species=8,n.cores = 2) summary(clade_disc_2) sensi_plot(clade_disc_2) sensi_plot(clade_disc_2, graph = "q12") sensi_plot(clade_disc_2, graph = "q21") ## End(Not run)
## Not run: #Load data: data("primates") #Create a binary trait factor primates$data$adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") clade_disc<-clade_discrete(data=primates$data,phy = primates$phy[[1]],model="SYM", trait.col = "adultMass_binary",clade.col="family",n.sim=30,n.species=10,n.cores = 2) summary(clade_disc) sensi_plot(clade_disc) sensi_plot(clade_disc, clade = "Cebidae", graph = "q12") #Change the evolutionary model, tree transformation or minimum number of species per clade clade_disc_2<-clade_discrete(data=primates$data,phy = primates$phy[[1]], model="ARD",transform="kappa", trait.col = "adultMass_binary",clade.col="family",n.sim=30, n.species=8,n.cores = 2) summary(clade_disc_2) sensi_plot(clade_disc_2) sensi_plot(clade_disc_2, graph = "q12") sensi_plot(clade_disc_2, graph = "q21") ## End(Not run)
Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis.
clade_phyglm( formula, data, phy, btol = 50, track = TRUE, clade.col, n.species = 5, n.sim = 100, ... )
clade_phyglm( formula, data, phy, btol = 50, track = TRUE, clade.col, n.species = 5, n.sim = 100, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, fits a phylogenetic
logistic regression model using phyloglm
and stores the
results. The impact of of a specific clade on model estimates is calculated by a
comparison between the full model (with all species) and the model without
the species belonging to a clade.
To account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
Currently, only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
clade_phyglm
detects influential clades based on
difference in intercept and/or estimate when removing a given clade compared
to the full model including all species.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function clade_phyglm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. alpha
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter are
reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades where deletion resulted in errors.
clade.col
: Which column was used to specify the clades?
Gustavo Paterno & Gijsbert Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, clade_phylm
,
influ_phyglm
, sensi_plot
## Not run: # Simulate Data: set.seed(6987) phy = rtree(150) x = rTrait(n=1,phy=phy) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) cla <- rep(c("A","B","C","D","E"), each = 30) dat = data.frame(y, x, cla) # Run sensitivity analysis: clade <- clade_phyglm(y ~ x, phy = phy, data = dat, n.sim = 30, clade.col = "cla") # To check summary results and most influential clades: summary(clade) # Visual diagnostics for clade removal: sensi_plot(clade) # Specify which clade removal to plot: sensi_plot(clade, "B") sensi_plot(clade, "C") sensi_plot(clade, "D") #The clade with the largest effect on slope and intercept ## End(Not run)
## Not run: # Simulate Data: set.seed(6987) phy = rtree(150) x = rTrait(n=1,phy=phy) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) cla <- rep(c("A","B","C","D","E"), each = 30) dat = data.frame(y, x, cla) # Run sensitivity analysis: clade <- clade_phyglm(y ~ x, phy = phy, data = dat, n.sim = 30, clade.col = "cla") # To check summary results and most influential clades: summary(clade) # Visual diagnostics for clade removal: sensi_plot(clade) # Specify which clade removal to plot: sensi_plot(clade, "B") sensi_plot(clade, "C") sensi_plot(clade, "D") #The clade with the largest effect on slope and intercept ## End(Not run)
Estimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis.
clade_phylm( formula, data, phy, model = "lambda", track = TRUE, clade.col, n.species = 5, n.sim = 100, ... )
clade_phylm( formula, data, phy, model = "lambda", track = TRUE, clade.col, n.species = 5, n.sim = 100, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, fits a phylogenetic
linear regression model using phylolm
and stores the
results. The impact of of a specific clade on model estimates is calculated by a
comparison between the full model (with all species) and the model without
the species belonging to a clade.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
clade_phylm
detects influential clades based on
difference in intercept and/or estimate when removing a given clade compared
to the full model including all species.
Currently, this function can only implement simple linear models (i.e.
). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function clade_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used)
are reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades where deletion resulted in errors.
clade.col
: Which column was used to specify the clades?
Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, samp_phylm
,
influ_phylm
, sensi_plot
,
sensi_plot
,clade_phyglm
,
## Not run: # Load data: data(primates) # run analysis: clade <- clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy[[1]], data = primates$data, n.sim = 30, clade.col = "family") # To check summary results and most influential clades: summary(clade) # Visual diagnostics for clade removal: sensi_plot(clade) # Specify which clade removal to plot: sensi_plot(clade, "Cercopithecidae") sensi_plot(clade, "Cebidae") ## End(Not run)
## Not run: # Load data: data(primates) # run analysis: clade <- clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy[[1]], data = primates$data, n.sim = 30, clade.col = "family") # To check summary results and most influential clades: summary(clade) # Visual diagnostics for clade removal: sensi_plot(clade) # Specify which clade removal to plot: sensi_plot(clade, "Cercopithecidae") sensi_plot(clade, "Cebidae") ## End(Not run)
Estimate the influence of clade removal on phylogenetic signal estimates
clade_physig( trait.col, data, phy, clade.col, n.species = 5, n.sim = 100, method = "K", track = TRUE, ... )
clade_physig( trait.col, data, phy, clade.col, n.species = 5, n.sim = 100, method = "K", track = TRUE, ... )
trait.col |
The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass"). |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
method |
Method to compute signal: can be "K" or "lambda". |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, estimates phylogenetic
signal (K or lambda) using phylosig
and stores the
results. The impact of a specific clade on signal estimates is calculated by the
comparison between the full data (with all species) and reduced data estimates
(without the species belonging to a clade).
To account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution of signal estimates expected by the removal of the same number of species as in a given clade. This is done by estimating phylogenetic signal without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
Output can be visualised using sensi_plot
.
The function clade_physig
returns a list with the following
components:
trait.col
: Column name of the trait analysed
full.data.estimates
: Phylogenetic signal estimate (K or lambda)
and the P value (for the full data).
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
phylogenetic signal (K or lambda) (estimate
), difference between simulation
signal and full data signal (DF
), the percentage of change
in signal compared to the full data estimate (perc
) and
p-value of the phylogenetic signal with the reduced data (pval
).
null.dist
: A data frame with estimates for the null distribution
of phylogenetic signal for all clades analysed.
data
: Original full dataset.
The argument "se" from phylosig
is not available in this function. Use the
argument "V" instead with intra_physig
to indicate the name of the column containing the standard
deviation or the standard error of the trait variable instead.
Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.
Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.
Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.
phylosig
,
clade_phylm
,sensi_plot
data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: clade <- clade_physig(trait.col = "logMass", data = alien.data, n.sim = 20, phy = alien.phy[[1]], clade.col = "family", method = "K") summary(clade) sensi_plot(clade, "Bovidae") sensi_plot(clade, "Sciuridae")
data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: clade <- clade_physig(trait.col = "logMass", data = alien.data, n.sim = 20, phy = alien.phy[[1]], clade.col = "family", method = "K") summary(clade) sensi_plot(clade, "Bovidae") sensi_plot(clade, "Sciuridae")
Fits models for trait evolution of continuous characters, detecting influential species.
influ_continuous( data, phy, model, bounds = list(), cutoff = 2, n.cores = NULL, track = TRUE, ... )
influ_continuous( data, phy, model, bounds = list(), cutoff = 2, n.cores = NULL, track = TRUE, ... )
data |
Data vector for a single continuous trait, with names matching tips in |
phy |
A phylogeny (class 'phylo') matching |
model |
The evolutionary model (see Details). |
bounds |
settings to constrain parameter estimates. See |
cutoff |
The cut-off parameter for influential species (see Details). |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time,
fits different models of continuous character evolution using fitContinuous
,
stores the results and calculates the effects on model parameters.
influ_continuous
detects influential species based on the standardised
difference in the rate parameter sigsq
and the optimisation parameter optpar
(e.g. lamda, kappa, alpha, depending on which model
is set), when removing
a given species compared to the full model including all species.
Species with a standardised difference above the value of
cutoff
are identified as influential.
Different evolutionary models from fitContinuous
can be used, i.e. BM
,OU
,
EB
, trend
, lambda
, kappa
, delta
and drift
.
See fitContinuous
for more details on evolutionary models.
The function tree_discrete
returns a list with the following
components:
call
: The function call
cutoff
: The value selected for cutoff
data
: The original full data vector
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
full.model.estimates
: Parameter estimates (rate of evolution sigsq
and where applicable optpar
), root state z0
,
AICc for the full model without deleted species.
influential_species
: List of influential species, based on standardised
difference in estimates for sigsq and optpar. Species are ordered from most influential to
less influential and only include species with a standardised difference > cutoff
.
sensi.estimates
: Parameter estimates (sigsq and optpar),(percentual) difference
in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq,
DIFoptpar, optpar.perc,sDIFoptpar),
AICc and z0 for each repeat with a species removed.
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: #Load data: data("primates") #Model trait evolution accounting for influential species adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) influ_cont<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "OU",cutoff = 2,n.cores = 2,track = TRUE) #Print summary statistics summary(influ_cont) sensi_plot(influ_cont) sensi_plot(influ_cont,graphs="sigsq") #' sensi_plot(influ_cont,graphs="optpar") #Use a different evolutionary model or cutoff influ_cont2<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "lambda",cutoff = 1.2,n.cores = 2,track = TRUE) summary(influ_cont2) sensi_plot(influ_cont2) influ_cont3<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "BM",cutoff = 2,n.cores = 2,track = TRUE) summary(influ_cont3) ## End(Not run)
## Not run: #Load data: data("primates") #Model trait evolution accounting for influential species adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) influ_cont<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "OU",cutoff = 2,n.cores = 2,track = TRUE) #Print summary statistics summary(influ_cont) sensi_plot(influ_cont) sensi_plot(influ_cont,graphs="sigsq") #' sensi_plot(influ_cont,graphs="optpar") #Use a different evolutionary model or cutoff influ_cont2<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "lambda",cutoff = 1.2,n.cores = 2,track = TRUE) summary(influ_cont2) sensi_plot(influ_cont2) influ_cont3<-influ_continuous(data = adultMass,phy = primates$phy[[1]], model = "BM",cutoff = 2,n.cores = 2,track = TRUE) summary(influ_cont3) ## End(Not run)
Fits models for trait evolution of discrete (binary) characters, detecting influential species.
influ_discrete( data, phy, model, transform = "none", bounds = list(), cutoff = 2, n.cores = NULL, track = TRUE, ... )
influ_discrete( data, phy, model, transform = "none", bounds = list(), cutoff = 2, n.cores = NULL, track = TRUE, ... )
data |
Data vector for a single binary trait, with names matching tips in |
phy |
A phylogeny (class 'phylo') matching |
model |
The Mkn model to use (see Details). |
transform |
The evolutionary model to transform the tree (see Details). Default is |
bounds |
settings to constrain parameter estimates. See |
cutoff |
The cut-off parameter for influential species (see Details). |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time,
fits a model of discrete character evolution using fitDiscrete
,
stores the results and calculates the effects on model parameters. Currently, only
binary discrete traits are supported.
influ_discrete
detects influential species based on the standardised
difference in q12 or q21 when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential.
Different character model from fitDiscrete
can be used, including ER
(equal-rates),
SYM
(symmetric), ARD
(all-rates-different) and meristic
(stepwise fashion).
Different transformations to the phylogenetic tree from fitDiscrete
can be used, i.e. none
,
EB
, lambda
, kappa
anddelta
.
See fitDiscrete
for more details on character models and tree transformations.
The function tree_discrete
returns a list with the following
components:
call
: The function call
cutoff
: The value selected for cutoff
data
: The original full data vector
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
full.model.estimates
: Parameter estimates (transition rates q12 and q21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for the full model.
influential_species
: List of influential species, based on standardised
difference in estimates for q12 and q21. Species are ordered from most influential to
less influential and only include species with a standardised difference > cutoff
.
sensi.estimates
: Parameter estimates (transition rates q12 and q21),,(percentual) difference
in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for each analysis with a species deleted.
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for influential species influ_binary<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "SYM",transform = "none",cutoff = 2,n.cores = 2,track = TRUE) #Print summary statistics summary(influ_binary) sensi_plot(influ_binary) #q12 and q21 are, as expected, exactly the same in symmetrical model. #Use a different evolutionary model. influ_binary2<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "SYM",transform = "delta",n.cores = 2,track = TRUE) summary(influ_binary2) sensi_plot(influ_binary2) #Or change the cutoff and transformation influ_binary3<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "ARD",transform = "none",cutoff = 1.2,n.cores = 2,track = TRUE) summary(influ_binary3) sensi_plot(influ_binary3) ## End(Not run)
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for influential species influ_binary<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "SYM",transform = "none",cutoff = 2,n.cores = 2,track = TRUE) #Print summary statistics summary(influ_binary) sensi_plot(influ_binary) #q12 and q21 are, as expected, exactly the same in symmetrical model. #Use a different evolutionary model. influ_binary2<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "SYM",transform = "delta",n.cores = 2,track = TRUE) summary(influ_binary2) sensi_plot(influ_binary2) #Or change the cutoff and transformation influ_binary3<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]], model = "ARD",transform = "none",cutoff = 1.2,n.cores = 2,track = TRUE) summary(influ_binary3) sensi_plot(influ_binary3) ## End(Not run)
Performs leave-one-out deletion analysis for phylogenetic logistic regression, and detects influential species.
influ_phyglm(formula, data, phy, btol = 50, cutoff = 2, track = TRUE, ...)
influ_phyglm(formula, data, phy, btol = 50, cutoff = 2, track = TRUE, ...)
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
btol |
Bound on searching space. For details see |
cutoff |
The cutoff value used to identify for influential species (see Details) |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time, fits a phylogenetic
logistic regression model using phyloglm
, stores the
results and detects influential species.
Currently only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
influ_phyglm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function influ_phyglm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (i.e. alpha
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in intercept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the standardised
difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(i.e. alpha
) are reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
Gustavo Paterno & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, samp_phyglm
,
influ_phylm
, sensi_plot
# Simulate Data: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: influ <- influ_phyglm(y ~ x, data = dat, phy = phy) # To check summary results and most influential species: summary(influ) # Visual diagnostics for clade removal: sensi_plot(influ)
# Simulate Data: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: influ <- influ_phyglm(y ~ x, data = dat, phy = phy) # To check summary results and most influential species: summary(influ) # Visual diagnostics for clade removal: sensi_plot(influ)
Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species.
influ_phylm( formula, data, phy, model = "lambda", cutoff = 2, track = TRUE, ... )
influ_phylm( formula, data, phy, model = "lambda", cutoff = 2, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
model |
The phylogenetic model to use (see Details). Default is |
cutoff |
The cutoff value used to identify for influential species (see Details) |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time, fits a phylogenetic
linear regression model using phylolm
, stores the
results and detects influential species.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
influ_phylm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function influ_phylm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in intercept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the standardised
difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used) are
reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
Gustavo Paterno & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, samp_phylm
,
influ_phyglm
, sensi_plot
# Load data: data(alien) # run analysis: influ <- influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], data = alien$data) # To check summary results: summary(influ) # Most influential speciesL influ$influential.species # Visual diagnostics sensi_plot(influ) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(influ, param = "estimate", graphs = 2)
# Load data: data(alien) # run analysis: influ <- influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], data = alien$data) # To check summary results: summary(influ) # Most influential speciesL influ$influential.species # Visual diagnostics sensi_plot(influ) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(influ, param = "estimate", graphs = 2)
Performs leave-one-out deletion analysis for phylogenetic signal estimates, and detects influential species for K or lambda.
influ_physig(trait.col, data, phy, method = "K", cutoff = 2, track = TRUE, ...)
influ_physig(trait.col, data, phy, method = "K", cutoff = 2, track = TRUE, ...)
trait.col |
The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass"). |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
method |
Method to compute signal: can be "K" or "lambda". |
cutoff |
The cutoff value used to identify for influential species (see Details) |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time, ans estimates phylogenetic
signal (K or lambda) using phylosig
, stores the
results and detects the most influential species.
influ_physig
detects influential species based on the standardised
difference in signal estimate (K or lambda) when removing a given species compared
to the full data estimate (with all species). Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences in signal estimate.
Output can be visualised using sensi_plot
.
The function influ_physig
returns a list with the following
components:
cutoff
: The value selected for cutoff
trait.col
: Column name of the trait analysed
full.data.estimates
: Phylogenetic signal estimate (K or lambda)
and the P value (for the full data).
influential_species
: List of influential species,
based on standardised difference in K or lambda.
Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
influ.physig.estimates
: A data frame with all simulation
estimates. Each row represents a deleted species
Columns report the calculated signal estimate (k
) or (lambda
),
difference between signal estimation of the reduced and full data
(DF
), the percentage of change in signal compared
to the full data signal (perc
) and p-value for the phylogenetic signal
test (pval
)
data
: Original full dataset.
The argument "se" from phylosig
is not available in this function. Use the
argument "V" instead with intra_physig
to indicate the name of the column containing the standard
deviation or the standard error of the trait variable instead.
Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.
Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.
Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.
phylosig
,
influ_phylm
,sensi_plot
## Not run: # Load data: data(alien) # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: influ <- influ_physig("logMass", data = alien.data, phy = alien.phy[[1]]) # To check summary results: summary(influ) # Most influential speciesL influ$influential.species # Visual diagnostics sensi_plot(influ) # You can specify which graph to print: sensi_plot(influ, graphs = 1) sensi_plot(influ, graphs = 2) ## End(Not run)
## Not run: # Load data: data(alien) # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: influ <- influ_physig("logMass", data = alien.data, phy = alien.phy[[1]]) # To check summary results: summary(influ) # Most influential speciesL influ$influential.species # Visual diagnostics sensi_plot(influ) # You can specify which graph to print: sensi_plot(influ, graphs = 1) sensi_plot(influ, graphs = 2) ## End(Not run)
Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis, while taking into account potential interactions with intraspecific variability.
intra_clade_phyglm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.intra = 2, Vx = NULL, distrib = "normal", x.transf = NULL, btol = 50, track = TRUE, ... )
intra_clade_phyglm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.intra = 2, Vx = NULL, distrib = "normal", x.transf = NULL, btol = 50, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
x.transf |
Transformation for the predictor variable (e.g. |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, fits a phylogenetic
logistic regression model using phyloglm
and stores the
results. The impact of of a specific clade on model estimates is calculated by the
comparison between the full model (with all species) and the model without
the species belonging to a clade. This operation is repeated n.intra
times for
simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function
generates a random value for each row in the dataset using the standard deviation or errors supplied, and
detect the influential species within that iteration.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
All phylogenetic models from phyloglm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phyloglm
for details.
clade_phyglm
detects influential clades based on
difference in intercept and/or slope when removing a given clade compared
to the full model including all species.
Currently, this function can only implement simple logistic models (i.e.
). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_clade_phyglm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used)
are reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades and/or iterations where deletion resulted in errors.
Gustavo Paterno, Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, intra_phyglm
,
clade_phyglm
, intra_clade_phylm
,
sensi_plot
## Not run: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) cla <- rep(c("A","B","C","D"), each = 25) dat = data.frame(y, x, z, cla) intra_clade <- intra_clade_phyglm(formula=y ~ x, data = dat, phy = phy, clade.col = "cla", n.sim = 30, n.intra = 3, x.transf = log, Vx = "z", distrib="normal") sensi_plot(intra_clade) sensi_plot(intra_clade, clade = "B", graphs = 2) ## End(Not run)
## Not run: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) cla <- rep(c("A","B","C","D"), each = 25) dat = data.frame(y, x, z, cla) intra_clade <- intra_clade_phyglm(formula=y ~ x, data = dat, phy = phy, clade.col = "cla", n.sim = 30, n.intra = 3, x.transf = log, Vx = "z", distrib="normal") sensi_plot(intra_clade) sensi_plot(intra_clade, clade = "B", graphs = 2) ## End(Not run)
Estimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis, while taking into account potential interactions with intraspecific variability.
intra_clade_phylm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.intra = 2, Vy = NULL, Vx = NULL, distrib = "normal", y.transf = NULL, x.transf = NULL, model = "lambda", track = TRUE, ... )
intra_clade_phylm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.intra = 2, Vy = NULL, Vx = NULL, distrib = "normal", y.transf = NULL, x.transf = NULL, model = "lambda", track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
Vy |
Name of the column containing the standard deviation or the standard error of the response
variable. When information is not available for one taxon, the value can be 0 or |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
y.transf |
Transformation for the response variable (e.g. |
x.transf |
Transformation for the predictor variable (e.g. |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, fits a phylogenetic
linear regression model using phylolm
and stores the
results. The impact of of a specific clade on model estimates is calculated by the
comparison between the full model (with all species) and the model without
the species belonging to a clade. This operation is repeated n.intra
times for
simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function
generates a random value for each row in the dataset using the standard deviation or errors supplied, and
detect the influential species within that iteration.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
clade_phylm
detects influential clades based on
difference in intercept and/or slope when removing a given clade compared
to the full model including all species.
Currently, this function can only implement simple linear models (i.e.
). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_clade_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used)
are reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades and/or iterations where deletion resulted in errors.
Gustavo Paterno, Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, intra_phylm
,
clade_phylm
, intra_clade_phyglm
,
sensi_plot
## Not run: #load data data(alien) intra_clade <- intra_clade_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]], data = alien$data, clade.col = "family", n.sim = 30, n.intra = 5, y.transf = log, x.transf = log, Vy="SD_gesta") summary(intra_clade) sensi_plot(intra_clade) sensi_plot(intra_clade, clade = "Bovidae", graphs = 2) sensi_plot(intra_clade, clade = "Mustelidae", graphs = 2) ## End(Not run)
## Not run: #load data data(alien) intra_clade <- intra_clade_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]], data = alien$data, clade.col = "family", n.sim = 30, n.intra = 5, y.transf = log, x.transf = log, Vy="SD_gesta") summary(intra_clade) sensi_plot(intra_clade) sensi_plot(intra_clade, clade = "Bovidae", graphs = 2) sensi_plot(intra_clade, clade = "Mustelidae", graphs = 2) ## End(Not run)
Performs leave-one-out deletion analysis for phylogenetic logistic regression, and detects influential species, while taking into account potential interactions with intraspecific variability.
intra_influ_phyglm( formula, data, phy, Vx = NULL, n.intra = 30, x.transf = NULL, distrib = "normal", cutoff = 2, btol = 50, track = TRUE, ... )
intra_influ_phyglm( formula, data, phy, Vx = NULL, n.intra = 30, x.transf = NULL, distrib = "normal", cutoff = 2, btol = 50, track = TRUE, ... )
formula |
The model formula: |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
x.transf |
Transformation for the predictor variable (e.g. |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
cutoff |
The cutoff value used to identify for influential species (see Details) |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic linear regression model using phylolm
, and detects
influential species by sequentially deleting one at a time. The regression is repeated n.intra
times for
simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function
generates a random value for each row in the dataset using the standard deviation or errors supplied, and
detect the influential species within that iteration.
influ_phylm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_influ_phylm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in intercept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for an iteration of resimulated
data. Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the standardised difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used) are
reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
When Vx exceeds X negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing n.intra
, changing the transformation type and/or checking the target species in output$sp.pb.
Setting n.intra
at high values can take a long time to execute, since the total number of iterations equals n.intra * nrow(data)
.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, intra_phyglm
,
influ_phyglm
,intra_influ_phylm
,sensi_plot
.
## Not run: #Generate data set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) dat = data.frame(y, x, z) # Run sensitivity analysis: intra_influ <- intra_influ_phyglm(formula = y ~ x, data = dat, phy = phy, Vx = "z", n.intra = 5,track = TRUE,distrib="normal",x.transf=NULL) # To check summary results and most influential species: summary(intra_influ) # Visual diagnostics for clade removal: sensi_plot(intra_influ) ## End(Not run)
## Not run: #Generate data set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) dat = data.frame(y, x, z) # Run sensitivity analysis: intra_influ <- intra_influ_phyglm(formula = y ~ x, data = dat, phy = phy, Vx = "z", n.intra = 5,track = TRUE,distrib="normal",x.transf=NULL) # To check summary results and most influential species: summary(intra_influ) # Visual diagnostics for clade removal: sensi_plot(intra_influ) ## End(Not run)
Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species, while taking into account potential interactions with intraspecific variability.
intra_influ_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 10, distrib = "normal", model = "lambda", cutoff = 2, track = TRUE, ... )
intra_influ_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 10, distrib = "normal", model = "lambda", cutoff = 2, track = TRUE, ... )
formula |
The model formula: |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
Vy |
Name of the column containing the standard deviation or the standard error of the response
variable. When information is not available for one taxon, the value can be 0 or |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
y.transf |
Transformation for the response variable (e.g. |
x.transf |
Transformation for the predictor variable (e.g. |
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
model |
The phylogenetic model to use (see Details). Default is |
cutoff |
The cutoff value used to identify for influential species (see Details) |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic linear regression model using phylolm
, and detects
influential species by sequentially deleting one at a time. The regression is repeated n.intra
times for
simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function
generates a random value for each row in the dataset using the standard deviation or errors supplied, and
detect the influential species within that iteration.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
influ_phylm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_influ_phylm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in intercept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for an iteration of resimulated
data. Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the standardised difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used) are
reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation.
Setting n.intra
at high values can take a long time to execute, since the total number of iterations
equals n.intra * nrow(data)
.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, intra_phylm
,
influ_phylm
,intra_influ_phyglm
,sensi_plot
.
## Not run: # Load data: data(alien) # run analysis: intra_influ <- intra_influ_phylm(formula = gestaLen ~ adultMass, phy = alien$phy[[1]], data=alien$data,model="lambda",y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL, n.intra=30,distrib = "normal") summary(intra_influ) sensi_plot(intra_influ) ## End(Not run)
## Not run: # Load data: data(alien) # run analysis: intra_influ <- intra_influ_phylm(formula = gestaLen ~ adultMass, phy = alien$phy[[1]], data=alien$data,model="lambda",y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL, n.intra=30,distrib = "normal") summary(intra_influ) sensi_plot(intra_influ) ## End(Not run)
Performs Phylogenetic logistic regression evaluating intraspecific variability in predictor variables.
intra_phyglm( formula, data, phy, Vx = NULL, n.intra = 30, x.transf = NULL, distrib = "normal", btol = 50, track = TRUE, ... )
intra_phyglm( formula, data, phy, Vx = NULL, n.intra = 30, x.transf = NULL, distrib = "normal", btol = 50, track = TRUE, ... )
formula |
The model formula: |
data |
Data frame containing species traits with species as row names. |
phy |
A phylogeny (class 'phylo', see ? |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
n.intra |
Number of times to repeat the analysis generating a random value for the predictor variable.
If NULL, |
x.transf |
Transformation for the predictor variable (e.g. |
distrib |
A character string indicating which distribution to use to generate a random value for the
predictor variable. Default is normal distribution: "normal" (function |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic logistic regression model using phyloglm
.
The regression is repeated n.intra
times. At each iteration the function generates a random value
for each row in the dataset using the standard deviation or error supplied and assuming a normal or uniform distribution.
To calculate means and se for your raw data, you can use the summarySE
function from the
package Rmisc
.
All phylogenetic models from phyloglm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phyloglm
for details.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_phyglm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for each regression.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters. sd_intra
is the standard deviation
due to intraspecific variation. CI_low
and CI_high
are the lower and upper limits
of the 95
sp.pb
: Species that caused problems with data transformation (see details above).
When Vx exceeds X negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing n.intra
, changing the transformation type and/or checking the target species in output$sp.pb.
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
# Simulate Data: set.seed(6987) phy = rtree(150) x = rTrait(n=1,phy=phy) x_sd = rnorm(150,mean = 0.8,sd=0.2) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x, x_sd) # Run phylogenetic logistic regression accounting for intraspecific variation: intra_glm <- intra_phyglm(y~x,Vx = "x_sd",data = dat,phy=phy,distrib = "normal") #Print summary of sensitivity analysis summary(intra_glm) head(intra_glm$sensi.estimates) #Visual output sensi_plot(intra_glm)
# Simulate Data: set.seed(6987) phy = rtree(150) x = rTrait(n=1,phy=phy) x_sd = rnorm(150,mean = 0.8,sd=0.2) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x, x_sd) # Run phylogenetic logistic regression accounting for intraspecific variation: intra_glm <- intra_phyglm(y~x,Vx = "x_sd",data = dat,phy=phy,distrib = "normal") #Print summary of sensitivity analysis summary(intra_glm) head(intra_glm$sensi.estimates) #Visual output sensi_plot(intra_glm)
Performs Phylogenetic linear regression evaluating intraspecific variability in response and/or predictor variables.
intra_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 30, distrib = "normal", model = "lambda", track = TRUE, ... )
intra_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 30, distrib = "normal", model = "lambda", track = TRUE, ... )
formula |
The model formula: |
data |
Data frame containing species traits and species names as row names. |
phy |
A phylogeny (class 'phylo', see ? |
Vy |
Name of the column containing the standard deviation or the standard error of the response
variable. When information is not available for one taxon, the value can be 0 or |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
y.transf |
Transformation for the response variable (e.g. |
x.transf |
Transformation for the predictor variable (e.g. |
n.intra |
Number of times to repeat the analysis generating a random value for response and/or predictor variables.
If NULL, |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic linear regression model using phylolm
.
The regression is repeated n.intra
times. At each iteration the function generates a random value
for each row in the dataset using the standard deviation or errors supplied and assuming a normal or uniform distribution.
To calculate means and se for your raw data, you can use the summarySE
function from the
package Rmisc
.
#' All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function intra_phylm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for each regression.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters. sd_intra
is the standard deviation
due to intraspecific variation. CI_low
and CI_high
are the lower and upper limits
of the 95
sp.pb
: Species that caused problems with data transformation (see details above).
When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing n.intra
, changing the transformation type and/or checking the target species in output$sp.pb.
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
# Load data: data(alien) # run PGLS accounting for intraspecific variation: intra <- intra_phylm(gestaLen ~ adultMass, y.transf = log, x.transf = log, phy = alien$phy[[1]], data = alien$data, Vy = "SD_gesta", n.intra = 30) # To check summary results: summary(intra) # Visual diagnostics sensi_plot(intra)
# Load data: data(alien) # run PGLS accounting for intraspecific variation: intra <- intra_phylm(gestaLen ~ adultMass, y.transf = log, x.transf = log, phy = alien$phy[[1]], data = alien$data, Vy = "SD_gesta", n.intra = 30) # To check summary results: summary(intra) # Visual diagnostics sensi_plot(intra)
Performs Phylogenetic signal estimates evaluating trait intraspecific variability
intra_physig( trait.col, data, phy, V = NULL, n.intra = 100, distrib = "normal", method = "K", track = TRUE )
intra_physig( trait.col, data, phy, V = NULL, n.intra = 100, distrib = "normal", method = "K", track = TRUE )
trait.col |
The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass"). |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo', see ? |
V |
Name of the column containing the standard deviation or the standard error of the trait
variable. When information is not available for one taxon, the value can be 0 or |
n.intra |
Number of times to repeat the analysis generating a random trait value.
If NULL, |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
method |
Method to compute signal: can be "K" or "lambda". |
track |
Print a report tracking function progress (default = TRUE) |
This function estimates phylogenetic signal using phylosig
.
The analysis is repeated n.intra
times. At each iteration the function generates a random value
for each row in the dataset using the standard deviation or errors supplied and assuming a normal or uniform distribution.
To calculate means and se for your raw data, you can use the summarySE
function from the
package Rmisc
.
Output can be visualised using sensi_plot
.
The function intra_physig
returns a list with the following
components:
Trait
: Column name of the trait analysed
data
: Original full dataset
intra.physig.estimates
: Run number, phylogenetic signal estimate
(lambda or K) and the p-value for each run with a different simulated datset.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for signal estimateCI_low
and CI_high
are the lower
and upper limits of the 95
The argument "se" from phylosig
is not available in this function. Use the
argument "V" instead with intra_physig
to indicate the name of the column containing the standard
deviation or the standard error of the trait variable instead.
Caterina Penone & Pablo Ariel Martinez & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4
Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.
Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.
Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.
## Not run: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Run sensitivity analysis: intra <- intra_physig(trait.col = "gestaLen", V = "SD_gesta" , data = alien.data, phy = alien.phy[[1]]) summary(intra) sensi_plot(intra) sensi_plot(intra, graphs = 1) sensi_plot(intra, graphs = 2) ## End(Not run)
## Not run: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Run sensitivity analysis: intra <- intra_physig(trait.col = "gestaLen", V = "SD_gesta" , data = alien.data, phy = alien.phy[[1]]) summary(intra) sensi_plot(intra) sensi_plot(intra, graphs = 1) sensi_plot(intra, graphs = 2) ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic logistic regression, while taking into account potential interactions with intraspecific variability.
intra_samp_phyglm( formula, data, phy, n.sim = 10, n.intra = 3, breaks = seq(0.1, 0.5, 0.1), btol = 50, Vx = NULL, distrib = "normal", x.transf = NULL, track = TRUE, ... )
intra_samp_phyglm( formula, data, phy, n.sim = 10, n.intra = 3, breaks = seq(0.1, 0.5, 0.1), btol = 50, Vx = NULL, distrib = "normal", x.transf = NULL, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
breaks |
A vector containing the percentages of species to remove. |
btol |
Bound on searching space. For details see |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
x.transf |
Transformation for the predictor variable (e.g. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic logistic regression, fits a phylogenetic
logistic regression model without these species using phylolm
,
repeats this many times (controlled by n.sim
), stores the results and
calculates the effects on model parameters.
This operation is repeated n.intra
times for simulated values of the dataset,
taking into account intraspecific variation. At each iteration, the function generates a
random value for each row in the dataset using the standard deviation or errors supplied, and
evaluates the effects of sampling within that iteration.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phyglm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.
Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, samp_phyglm
,
intra_phyglm
,intra_samp_phylm
,
sensi_plot
## Not run: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) dat = data.frame(y, x, z) #Run sensitivity analysis: intra_samp <- intra_samp_phyglm(formula = y ~ x, data = dat, phy = phy, n.sim=10, n.intra = 3, breaks=seq(.1,.5,.1), Vx = "z", distrib="normal", x.transf=NULL) summary(intra_samp) sensi_plot(intra_samp) ## End(Not run)
## Not run: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1)) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x)) dat = data.frame(y, x, z) #Run sensitivity analysis: intra_samp <- intra_samp_phyglm(formula = y ~ x, data = dat, phy = phy, n.sim=10, n.intra = 3, breaks=seq(.1,.5,.1), Vx = "z", distrib="normal", x.transf=NULL) summary(intra_samp) sensi_plot(intra_samp) ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic linear regression, while taking into account potential interactions with intraspecific variability.
intra_samp_phylm( formula, data, phy, n.sim = 10, n.intra = 3, breaks = seq(0.1, 0.5, 0.1), model = "lambda", Vy = NULL, Vx = NULL, distrib = "normal", y.transf = NULL, x.transf = NULL, track = TRUE, ... )
intra_samp_phylm( formula, data, phy, n.sim = 10, n.intra = 3, breaks = seq(0.1, 0.5, 0.1), model = "lambda", Vy = NULL, Vx = NULL, distrib = "normal", y.transf = NULL, x.transf = NULL, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
n.intra |
Number of datasets resimulated taking into account intraspecific variation (see: |
breaks |
A vector containing the percentages of species to remove. |
model |
The phylogenetic model to use (see Details). Default is |
Vy |
Name of the column containing the standard deviation or the standard error of the response
variable. When information is not available for one taxon, the value can be 0 or |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
y.transf |
Transformation for the response variable (e.g. |
x.transf |
Transformation for the predictor variable (e.g. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic linear regression, fits a phylogenetic
linear regression model without these species using phylolm
,
repeats this many times (controlled by n.sim
), stores the results and
calculates the effects on model parameters.
This operation is repeated n.intra
times for simulated values of the dataset,
taking into account intraspecific variation. At each iteration, the function generates a
random value for each row in the dataset using the standard deviation or errors supplied, and
evaluates the effects of sampling within that iteration.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.
Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
,samp_phylm
,
intra_phylm
, intra_samp_phyglm
,
sensi_plot
## Not run: # Load data: data(alien) # Run analysis: samp <- intra_samp_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]], y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL, data = alien$data, n.intra = 5, n.sim=10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics sensi_plot(samp) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)
## Not run: # Load data: data(alien) # Run analysis: samp <- intra_samp_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]], y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL, data = alien$data, n.intra = 5, n.sim=10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics sensi_plot(samp) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)
Combines phylogeny and data to ensure that tips in phylogeny match data and that observations with missing values are removed. This function uses variables provided in the 'formula' argument to:
Remove NA's: Check if there is any row with NA in the variables included in the formula. All rows containing NA will be removed from the data
Match data and phy: Check if tips from phylogeny match rownames in data. Tips not present in data and phy will be removed from the phylogeny and data
Return matched data and phy: The returned data has no NA in the variables included in 'formula' and only rows that match phylogeny tips. Returned phy has only tips that match data
Used internally in samp_phylm
, samp_phyglm
, clade_phylm
,
clade_phyglm
, intra_phylm
, intra_phyglm
, tree_phylm
,
tree_phyglm
and all function analysing interactions.
Users can also directly use this function to combine a phylogeny and a dataset.
match_dataphy(formula, data, phy, verbose = TRUE, ...)
match_dataphy(formula, data, phy, verbose = TRUE, ...)
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo' or 'multiphylo') |
verbose |
Print the number of species that match data and phylogeny and warnings. We highly recommend to use the default (verbose = T), but warning and information can be silenced for advanced use. |
... |
Further arguments to be passed to |
This function uses all variables provided in the 'formula' to match data and phylogeny. To avoid cropping the full dataset, 'match_dataphy' searches for NA values only on variables provided by formula. Missing values on other variables, not included in 'formula', will not be removed from data. If no species names are provided as row names in the dataset but the number of rows in the dataset is the same as the number of tips in the phylogeny, the function assumes that the dataset and the phylogeny are in the same order.
This ensures consistency between data and phylogeny only for the variables that are being used in the model (set by 'formula').
If phy is a 'multiphylo' object, all phylogenies will be cropped to match data. But the dataset order will only match the first tree provided. The returned phylogeny will be a 'multiphylo' object.
The function match_dataphy
returns a list with the following
components:
data
: Cropped dataset matching phylogeny
phy
: Cropped phylogeny matching data
dropped
: Species dropped from phylogeny and removed from data.
If tips are removed from the phylogeny and data or if rows containing missing values are removed from data, a message will be printed with the details. Further, the final number of species that match data and phy will always be reported by a message.
Caterina Penone & Gustavo Paterno
This function is largely inspired by the function comparative.data
in caper package
David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac and Will Pearse
(2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2.
http://CRAN.R-project.org/package=caper
# Load data: data(alien) head(alien$data) # Match data and phy based on model formula: comp.data <- match_dataphy(gestaLen ~ homeRange, data = alien$data, alien$phy[[1]]) # Check data: head(comp.data$data) # Check phy: comp.data$phy # See species dropped from phy or data: comp.data$dropped # Example2: # Match data and phy based on model formula: comp.data2 <- match_dataphy(gestaLen ~ adultMass, data = alien$data, alien$phy) # Check data (missing data on variables not included in the formula are preserved) head(comp.data2$data) # Check phy: comp.data2$phy # See species dropped from phy or data: comp.data2$dropped
# Load data: data(alien) head(alien$data) # Match data and phy based on model formula: comp.data <- match_dataphy(gestaLen ~ homeRange, data = alien$data, alien$phy[[1]]) # Check data: head(comp.data$data) # Check phy: comp.data$phy # See species dropped from phy or data: comp.data$dropped # Example2: # Match data and phy based on model formula: comp.data2 <- match_dataphy(gestaLen ~ adultMass, data = alien$data, alien$phy) # Check data (missing data on variables not included in the formula are preserved) head(comp.data2$data) # Check phy: comp.data2$phy # See species dropped from phy or data: comp.data2$dropped
Calculates D statistic (Fritz & Purvis 2010), a measure of phylogenetic
signal, for missing data. Missingness is recoded into a binary variable
(1=missing, 0=non missing). This function is an adaptation of
phylo.d
for missing data.
miss.phylo.d(data, phy, ...)
miss.phylo.d(data, phy, ...)
data |
Data frame containing species traits with species as row names. |
phy |
A phylogeny (class 'phylo', see ? |
... |
Further arguments to be passed to |
This function builds on phylo.d
to calculate a phylogenetic signal
in missing data. The variable of interest, usually a trait, is recoded into a binary variable
(1=missing data, 0=non missing data). Then the phylo.d
function tests the estimated
D value for significant departure from both random association and the clumping expected under a Brownian
evolution threshold model (Fritz & Purvis, 2010).
Output can be visualised using print()
and plot()
The function miss.phylo.d
returns an object of class "phylo.d" with the following
components, for complete list of arguments see phylo.d
:
DEstimate
: The estimated D value
Pval1
: A p value, giving the result of testing whether D is significantly different from one
Pval0
: A p value, giving the result of testing whether D is significantly different from zero
The function also prints the percentage of missing data per variable in the dataset.
Caterina Penone & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Fritz, S. A. and Purvis, A. (2010). Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology, 24(4):1042-1051.
David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac and Will Pearse (2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. https://CRAN.R-project.org/package=caper
# Load caper: library(caper) # Load data data(primates) data<-alien$data phy=alien$phy[[1]] # Test phylogenetic signal for missing data: sexNAsig <- miss.phylo.d(data,phy,binvar=homeRange) print(sexNAsig) plot(sexNAsig) massNAsig <- miss.phylo.d(data,phy,binvar=adultMass) print(massNAsig) plot(massNAsig)
# Load caper: library(caper) # Load data data(primates) data<-alien$data phy=alien$phy[[1]] # Test phylogenetic signal for missing data: sexNAsig <- miss.phylo.d(data,phy,binvar=homeRange) print(sexNAsig) plot(sexNAsig) massNAsig <- miss.phylo.d(data,phy,binvar=adultMass) print(massNAsig) plot(massNAsig)
A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(primates)
data(primates)
A data frame with 95 rows and 3 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
sexMaturity: Age when individuals are first physically capable of reproducing (days)
homeRange: Mean home range (km)
A multiphylo containing 101 trees for 95 primate species.
Data downloaded from: http://esapubs.org/archive/ecol/E090/184/
Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(primates)
data(primates)
A data frame with 95 rows and 3 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
sexMaturity: Age when individuals are first physically capable of reproducing (days)
homeRange: Mean home range (km)
A multiphylo containing 101 trees for 95 primate species.
Data downloaded from: http://esapubs.org/archive/ecol/E090/184/
Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).
data(primates)
data(primates)
A data frame with 95 rows and 3 variables:
family: Taxonomic family
adultMass: Mean adult body mass (g)
sexMaturity: Age when individuals are first physically capable of reproducing (days)
homeRange: Mean home range (km)
A multiphylo containing 101 trees for 95 primate species.
Data downloaded from: http://esapubs.org/archive/ecol/E090/184/
Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1
Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.
Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.
Fits models for trait evolution of continuous characters, evaluating sampling uncertainty.
samp_continuous( data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model, n.cores = NULL, bounds = list(), track = TRUE, ... )
samp_continuous( data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model, n.cores = NULL, bounds = list(), track = TRUE, ... )
data |
Data vector for a single binary trait, with names matching tips in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each |
breaks |
A vector containing the percentages of species to remove. |
model |
The evolutionary model (see Details). |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
bounds |
settings to constrain parameter estimates. See |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by breaks
),
fits different models of continuous character evolution using fitContinuous
,
repeats this this many times (controlled by n.sim
), stores the results and calculates
the effects on model parameters.
Different evolutionary models from fitContinuous
can be used, i.e. BM
,OU
,
EB
, trend
, lambda
, kappa
, delta
and drift
.
See fitContinuous
for more details on character models and tree transformations.
Output can be visualised using sensi_plot
.
The function tree_continuous
returns a list with the following
components:
call
: The function call
data
: The original full data vector
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
full.model.estimates
: Parameter estimates (rate of evolution sigsq
and where applicable optpar
), root state z0
,
AICc for the full model without deleted species.
break.summary.tab
: Summary per break
of the mean and median effects
of species removal on percentage and absolute change parameter estimates.
sensi.estimates
: Parameter estimates (sigsq and optpar),(percentual) difference
in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq,
DIFoptpar, optpar.perc,sDIFoptpar),
AICc and z0 for each repeat with random species removed.
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
## Not run: #Load data: data("primates") #Model trait evolution accounting for sampling size adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "OU",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2, track = TRUE) #Print summary statistics summary(samp_cont) sensi_plot(samp_cont) sensi_plot(samp_cont, graphs = 1) #Use a different evolutionary model samp_cont2<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "kappa",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE) summary(samp_cont2) sensi_plot(samp_cont2) sensi_plot(samp_cont2, graphs = 2) samp_cont3<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "BM",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE) summary(samp_cont3) ## End(Not run)
## Not run: #Load data: data("primates") #Model trait evolution accounting for sampling size adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "OU",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2, track = TRUE) #Print summary statistics summary(samp_cont) sensi_plot(samp_cont) sensi_plot(samp_cont, graphs = 1) #Use a different evolutionary model samp_cont2<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "kappa",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE) summary(samp_cont2) sensi_plot(samp_cont2) sensi_plot(samp_cont2, graphs = 2) samp_cont3<-samp_continuous(data = adultMass,phy = primates$phy[[1]], model = "BM",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE) summary(samp_cont3) ## End(Not run)
Fits models for trait evolution of discrete (binary) characters, evaluating sampling uncertainty.
samp_discrete( data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model, transform = "none", bounds = list(), n.cores = NULL, track = TRUE, ... )
samp_discrete( data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model, transform = "none", bounds = list(), n.cores = NULL, track = TRUE, ... )
data |
Data vector for a single binary trait, with names matching tips in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each |
breaks |
A vector containing the percentages of species to remove. |
model |
The Mkn model to use (see Details). |
transform |
The evolutionary model to transform the tree (see Details). Default is |
bounds |
settings to constrain parameter estimates. See |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by breaks
),
fits different models of discrete character evolution using fitDiscrete
,
repeats this this many times (controlled by n.sim
), stores the results and calculates
the effects on model parameters Currently, only binary discrete traits are supported.
Different character model from fitDiscrete
can be used, including ER
(equal-rates),
SYM
(symmetric), ARD
(all-rates-different) and meristic
(stepwise fashion).
Transformations to the phylogenetic tree from fitDiscrete
can be used, i.e. none
,
EB
, lambda
, kappa
anddelta
.
See fitDiscrete
for more details on character models and tree transformations.
Output can be visualised using sensi_plot
.
The function tree_discrete
returns a list with the following
components:
call
: The function call
data
: The original full data vector
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
full.model.estimates
: Parameter estimates (transition rates q12 and q21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for the full model without deleted species.
break.summary.tab
: Summary per break
of the mean and median effects
of species removal on percentage and absolute change in parameters q12 and q21.
sensi.estimates
: Parameter estimates (transition rates q12 and q21),(percentual) difference
in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for each analysis with a species deleted.
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for sampling size samp_binary<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]], n.sim=25,breaks=seq(.1,.3,.1),model = "SYM",transform = "none",n.cores = 2,track = TRUE) #Print summary statistics summary(samp_binary) sensi_plot(samp_binary) sensi_plot(samp_binary,graphs=1) sensi_plot(samp_binary,graphs=2) #Use a different evolutionary model or transformation samp_binary2<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]], n.sim=25,breaks=seq(.1,.3,.1),model = "ARD",transform = "lambda",n.cores = 2,track = TRUE) summary(samp_binary2) sensi_plot(samp_binary2) sensi_plot(samp_binary2,graphs=1) sensi_plot(samp_binary2,graphs=3) ## End(Not run)
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for sampling size samp_binary<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]], n.sim=25,breaks=seq(.1,.3,.1),model = "SYM",transform = "none",n.cores = 2,track = TRUE) #Print summary statistics summary(samp_binary) sensi_plot(samp_binary) sensi_plot(samp_binary,graphs=1) sensi_plot(samp_binary,graphs=2) #Use a different evolutionary model or transformation samp_binary2<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]], n.sim=25,breaks=seq(.1,.3,.1),model = "ARD",transform = "lambda",n.cores = 2,track = TRUE) summary(samp_binary2) sensi_plot(samp_binary2) sensi_plot(samp_binary2,graphs=1) sensi_plot(samp_binary2,graphs=3) ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in phylogenetic logistic regression.
samp_phyglm( formula, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), btol = 50, track = TRUE, ... )
samp_phyglm( formula, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), btol = 50, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
breaks |
A vector containing the percentages of species to remove. |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic logistic regression, fits a phylogenetic
logistic regression model without these species using phyloglm
,
repeats this many times (controlled by n.sim
), stores the results and
calculates the effects on model parameters.
Only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.
Gustavo Paterno & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
#' Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, samp_phylm
,
influ_phyglm
, sensi_plot
# Simulate Data: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: samp <- samp_phyglm(y ~ x, data = dat, phy = phy, n.sim = 10) # To check summary results and most influential species: summary(samp) ## Not run: # Visual diagnostics for clade removal: sensi_plot(samp) ## End(Not run)
# Simulate Data: set.seed(6987) phy = rtree(100) x = rTrait(n=1,phy=phy) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: samp <- samp_phyglm(y ~ x, data = dat, phy = phy, n.sim = 10) # To check summary results and most influential species: summary(samp) ## Not run: # Visual diagnostics for clade removal: sensi_plot(samp) ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic linear regression.
samp_phylm( formula, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model = "lambda", track = TRUE, ... )
samp_phylm( formula, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), model = "lambda", track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
breaks |
A vector containing the percentages of species to remove. |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic linear regression, fits a phylogenetic
linear regression model without these species using phylolm
,
repeats this many times (controlled by n.sim
), stores the results and
calculates the effects on model parameters.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
#' @note Please be aware that dropping species may reduce power to detect
significant slopes/intercepts and may partially be responsible for a potential
effect of species removal on p-values. Please also consult standardised differences
in the (summary) output.
Gustavo Paterno & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, samp_phyglm
,
influ_phylm
,sensi_plot
# Load data: data(alien) # Run analysis: samp <- samp_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], data = alien$data, n.sim = 10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics ## Not run: sensi_plot(samp) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(samp, graphs = 1, param = "estimate") sensi_plot(samp, graphs = 2, param = "intercept") ## End(Not run)
# Load data: data(alien) # Run analysis: samp <- samp_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], data = alien$data, n.sim = 10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics ## Not run: sensi_plot(samp) # You can specify which graph and parameter ("estimate" or "intercept") to print: sensi_plot(samp, graphs = 1, param = "estimate") sensi_plot(samp, graphs = 2, param = "intercept") ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on phylogenetic signal estimates
samp_physig( trait.col, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), method = "K", track = TRUE, ... )
samp_physig( trait.col, data, phy, n.sim = 30, breaks = seq(0.1, 0.5, 0.1), method = "K", track = TRUE, ... )
trait.col |
The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass"). |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times to repeat species random removal for each
|
breaks |
A vector containing the percentages of species to remove. |
method |
Method to compute signal: can be "K" or "lambda". |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full data, estimates phylogenetic
signal for a given trait (K or lambda) without these species using
phylosig
, then
repeats the analysis many times (controlled by n.sim
), stores the results and
calculates the effect of random species removal on phylogenetic signal estimates.
Output can be visualised using sensi_plot
.
The function samp_phylosig
returns a list with the following
components:
Trait
: Column name of the trait analysed
full.model.estimates
: Phylogenetic signal (K or lambda) and
p-value using the full dataset (without deleted species). See
phylosig
for details.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated signal estimate (estimate
),
difference between reduced data signal estimate and full data signal (DF
),
the percentage of change in signal compared to the full data estimate (perc
)
and signal p-value for the reduced data estimate(pval
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
phylogenetic signal over all repetitions with reduced data sets.
data
: Original full dataset used in the analysis.
#' @note Please be aware that dropping species may reduce power to detect
significant signal and may partially be responsible for a potential
effect of species removal on p-values. Please also consult standardised differences
in the (summary) output.
The argument "se" from phylosig
is not available in this function. Use the
argument "V" instead with intra_physig
to indicate the name of the column containing the standard
deviation or the standard error of the trait variable instead.
Gustavo Paterno & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.
Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.
Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.
phylosig
,
samp_phylm
,sensi_plot
## Not run: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: samp <- samp_physig(trait.col = "logMass", data = alien.data, n.sim = 30, phy = alien.phy[[1]]) summary(samp) sensi_plot(samp) sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)
## Not run: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: samp <- samp_physig(trait.col = "logMass", data = alien.data, n.sim = 30, phy = alien.phy[[1]]) summary(samp) sensi_plot(samp) sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)
Generic function for plotting results from any sensitivity analysis performed with 'sensiPhy'
sensi_plot(x, ...)
sensi_plot(x, ...)
x |
any output from the sensiPhy package. |
... |
further arguments to methods |
sensi_plot recognize and print different sets of graphs depending on the function that generated 'x'. See the links below for details about the graphs generated for each sensiPhy function:
PGLS regressions (single uncertainty):
clade_phylm: sensi_plot.sensiClade
influ_phylm: sensi_plot.sensiInflu
samp_phylm: sensi_plot.sensiSamp
intra_phylm: sensi_plot.sensiIntra
tree_phylm: sensi_plot.sensiTree
PGLS regressions (interacting uncertainties):
tree_intra_phylm: sensi_plot.sensiTree_Intra
intra_clade_phylm: sensi_plot.sensiIntra_Clade
intra_influ_phylm: sensi_plot.sensiIntra_Influ
intra_samp_phylm: sensi_plot.sensiIntra_Samp
tree_clade_phylm: sensi_plot.sensiTree_Clade
tree_influ_phylm: sensi_plot.sensiTree_Influ
tree_samp_phylm: sensi_plot.sensiTree_Samp
Phylogenetic signal:
clade_physig: sensi_plot.clade.physig
influ_physig: sensi_plot.influ.physig
samp_physig: sensi_plot.samp.physig
tree_physig: sensi_plot.tree.physig
intra_physig: sensi_plot.intra.physig
trait evolution (continuous & discrete characters):
clade_continuous & _discrete sensi_plot.sensiClade.TraitEvol
influ_continuous & _discrete sensi_plot.sensiInflu.TraitEvol
tree_continuous & _discrete sensi_plot.sensiTree.TraitEvol
samp_continuous & _discrete sensi_plot.sensiSamp.TraitEvol
Gustavo Paterno
The function 'multiplot', developed by Winston Chang, is used inside sensi_plot to print multiple graphs in one frame. The source code is available here: http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
plot_clade_physig
Plot results from clade_physig
## S3 method for class 'clade.physig' sensi_plot(x, clade = NULL, ...)
## S3 method for class 'clade.physig' sensi_plot(x, clade = NULL, ...)
x |
output from |
clade |
The name of the clade to be evaluated (see details) |
... |
further arguments to methods. |
For 'x' from clade_physig:
Graph 1: Distribution of the simulated signal estimates (Null distribution for a given clade sample size). The red dashed line represents the estimated signal for the reduced data (without the focal clade) and the black line represents the signal estimate for the full data.
Gustavo Paterno
sensi_plot.influ_physig
Plot results from influ_physig
## S3 method for class 'influ.physig' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'influ.physig' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1, 2) |
... |
further arguments to methods. |
For 'x' from influ_physig:
Graph 1: Distribution of estimated phylogenetic signal (K or lambda) for each simulation (leave-one-out deletion). Dashed red vertical line represents the original estimate of phylogenetic signal with the full data (with all species).
Graph 2: Distribution of P values for the phylogenetic signal (K or lambda) for each simulation (leave-one-out deletion). Red vertical line represents the alpha significance level = 0.05.
Gustavo Paterno
sensi_plot_intra.physig
Plot results from intra_physig
.
## S3 method for class 'intra.physig' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'intra.physig' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1 or 2) |
... |
further arguments to methods |
For 'x' from intra_physig
Graphs 1: Distribution of estimated phylogenetic signal (lambda or K) for each simulation Red vertical line represents the average signal among all estimates.
Graph 2: Distribution of p-values for the phylogenetic signal (K or lambda) for each simulation. Red vertical line represents the alpha significance level = 0.05.
Caterina Penone and Gustavo Paterno
ggplot
, intra_phylm
intra_phylm
plot_samp_phylm
Plot results from samp_physig
## S3 method for class 'samp.physig' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'samp.physig' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2 and 3 ) |
... |
further arguments to methods |
For 'x' from samp_physig:
Graph 1: Estimated phylogenetic signal for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full data estimate (blue = lower than 5, orange = between 5 and 10 and red = higher than 10). The red horizontal line represents the original phylogenetic signal estimated from the full model (with all species).
Graph 2: The proportion of estimated signal in each category across the percentage of species removed.
Graph 3: The percentage of significant signal estimates across the percentage of species removed.
Gustavo Paterno
ggplot
, samp_phylm
samp_physig
Plot results from clade_phylm
and clade_phyglm
## S3 method for class 'sensiClade' sensi_plot(x, clade = NULL, ...)
## S3 method for class 'sensiClade' sensi_plot(x, clade = NULL, ...)
x |
output from |
clade |
The name of the clade to be evaluated (see details) |
... |
further arguments to methods. |
For 'x' from clade_phylm or clade_phyglm:
Graph 1: The original scatterplot (with the
full dataset) and a comparison between the regression lines of the full dataset
and the rerun without the selected clade (set by
clade
). For further
details about this method, see clade_phylm
.
Species from the selected clade are represented in red (removed species), black
solid line represents the regression with the full model and red dashed line represents
the regression of the model without the species from the selected clade.
To check the available clades to plot, see x$sensi.estimates$clade
in the object returned from clade_phylm
or clade_phyglm
.
Graph 2: Distribution of the simulated slopes (Null distribution for a given clade sample size). The red dashed line represents the estimated slope for the reduced model (without the focal clade) and the black line represents the slope for the full model.
Gustavo Paterno
Plot results from clade_discrete
and clade_continuous
## S3 method for class 'sensiClade.TraitEvol' sensi_plot(x, clade = NULL, graph = "all", ...)
## S3 method for class 'sensiClade.TraitEvol' sensi_plot(x, clade = NULL, graph = "all", ...)
x |
output from |
clade |
The name of the clade to be evaluated (see details) |
graph |
The graph to be printed. Default |
... |
further arguments to methods. |
For 'x' from clade_discrete or clade_continuous:
Graph 1: Distribution of the simulated parameter estimates (Null distribution for a given clade sample size). The red dashed line represents the estimated signal for the reduced data (without the focal clade) and the black line represents the signal estimate for the full data.
Gustavo Paterno & Gijsbert Werner
clade_continuous
or clade_discrete
plot_influ_phylm
Plot results from influ_phylm
and
influ_phyglm
## S3 method for class 'sensiInflu' sensi_plot(x, graphs = "all", param = "estimate", ...)
## S3 method for class 'sensiInflu' sensi_plot(x, graphs = "all", param = "estimate", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
param |
choose which parameter ("intercept" or "estimate" should be printed) |
... |
further arguments to methods |
For 'x' from influ_phylm or influ_phyglm:
Graph 1: Distribution of estimated slopes (estimates) or intercepts for each simulation (leave-one-out deletion). Red vertical line represents the original slope or intercept from the full model (with all species).
Graph 2: Original regression plot (). Standardized
difference in slope or intercept is represented by a continuous size scale.
The names of the most influential species (sDF > cutoff) are ploted in the
graph.
Graph 3: Distribution of standardized difference in slope or intercept. Red
colour indicates influential species (with a standardised difference above
the value of cutoff
).
Graph 4: Distribution of the percentage of change in slope or intercept.
Gustavo Paterno
sensi_plot.sensiTree.TraitEvol
Plot results from influ_discrete
and influ_continuous
.
## S3 method for class 'sensiInflu.TraitEvol' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiInflu.TraitEvol' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", "q12", "q21", "aic" or" "optpar") |
... |
further arguments to methods |
Gijsbert Werner
ggplot
, influ_discrete
influ_continuous
The following graphs are printed.
Graph aicc: Distribution of estimated AICc-values across all single-species deletions. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
Graph optpar: Distribution of estimated values for optimisation parameter specified using 'transform' (if applicable) Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
Additionally, only for tree_discrete
the function creates the following graphs.
Graph q12: Distribution of estimated parameter values for transition rates q12 across all single-species deletions. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
Graph q21: Distribution of estimated parameter values for transition rates q21. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
While only for tree_continuous
the function creates the following graphs.
Graph sigsq: Distribution of estimated parameter values for rate of evolution sigsq across all single-species deletions. . Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
Graph z0: Distribution of estimated parameter values for z0. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.
plot_tree.intra_phylm
Plot results from tree_phylm
,
intra_phylm
and intra_phyglm
## S3 method for class 'sensiIntra' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiIntra' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1, 2 or 3) |
... |
further arguments to methods |
For 'x' from tree_phylm
, tree_phyglm
,
intra_phylm
or intra_phyglm
:
Graphs 1 and 2: Distribution of estimated slopes (estimates) and intercepts for each tree (for tree_phylm
) or
value generated within a given interval (intra_phylm
)
Red vertical line represents the mean estimate or intercept for all models.
Graph 3: Scatterplot with mean regression (black line) and standard deviation of the regression (red dotted lines).
Caterina Penone and Gustavo Paterno
ggplot
, tree_phylm
intra_phylm
Plot results from intra_clade_phylm
and intra_clade_phyglm
## S3 method for class 'sensiIntra_Clade' sensi_plot(x, clade = NULL, graphs = "all", ...)
## S3 method for class 'sensiIntra_Clade' sensi_plot(x, clade = NULL, graphs = "all", ...)
x |
output from |
clade |
The name of the clade to be evaluated (see details) |
graphs |
Choose which graph should be printed on the output ("all", 1 or 2). Defaults to "all". |
... |
further arguments to methods. |
For 'x' from intra_clade_phylm or intra_clade_phyglm:
Graph 1: Estimated slopes after clade removal (reduced data) across multiple simulations. Small dots represent estimates reruns between simulations while larger dots represents the average estimate between all simulations for each clade. The solid black line represents the average slope estimate among trees using the full dataset.
Graph 2: The effect of clade removal on slope estimate across all individual simulations for each clade analyzed. The black line indicates estimates with the full dataset while the red line represent estimates without the focal clade (reduced data) across different simulation The blue dots represent null expectation estimates after removing the same number of species of the focal clade, with dots falling outside the red line area indicating a larger than expected absolute effect.
Gustavo Paterno, Caterina Penone
sensi_plot.intra_influ
Plot results from intra_influ_phylm
and
intra_influ_phyglm
## S3 method for class 'sensiIntra_Influ' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiIntra_Influ' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
... |
further arguments to methods |
For 'x' from intra_influ_phylm or intra_influ_phyglm:
Graph 1: Most common influential species on model estimates. Percentage of iterations (n.tree or n.intra) where the removal of individual species caused significant change in model estimate (sDIFestimate > cutoff).
Graph 2: Shift on model estimate after species removal for most influential species. Small red dots represent individual reruns between different trees or simulations while large red dots represent the average DIFestimate among all iterations.
Gustavo Paterno, Caterina Penone
intra_influ_phylm
;
intra_influ_phyglm
sensi_plot.sensiIntra_Samp
Plot results from sensiIntra_Samp_phylm
and
sensiIntra_Samp_phyglm
## S3 method for class 'sensiIntra_Samp' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiIntra_Samp' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
... |
further arguments to methods |
For 'x' from sensiIntra_Samp_phylm or sensiIntra_Samp_phyglm:
Graph 1: : Estimated estimates for each simulation across percentages of species removed. Small red dots represent individual estimates for each iteration (tree or intra) across percentages of removed species. Large dots represent the average among these estimates within each iteration and percentage of species removal. Different estimates within the same percentage interval represent different iterations (tree or intra).
Graph 2: : The percentage of significant estimates (p < 0.05) across the percentage of species removed. Small red dots represent individual iterations (tree or intra) while large dots represent the average among these iterations.
Gustavo Paterno
ggplot
, samp_phylm
samp_phyglm
plot_samp_phylm
Plot results from samp_phylm
and
influ_phyloglm
## S3 method for class 'sensiSamp' sensi_plot(x, graphs = "all", param = "estimate", ...)
## S3 method for class 'sensiSamp' sensi_plot(x, graphs = "all", param = "estimate", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
param |
choose which model parameter should be ploted ("intercept" or "estimate") |
... |
further arguments to methods |
For 'x' from samp_phylm or samp_phyglm:
Graph 1: Estimated slopes or intercepts for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full model (blue = lower than 5, orange = between 5 and 10 and red = higher than 10). The red horizontal line represents the original slope or intercept from the full model (with all species).
Graph 2: The proportion of estimated slopes and intercepts in each category across the percentage of species removed.
Graph 3: Estimated phylogenetic model parameter for each simulation across the percentage of species removed.
Graph 4: The percentage of significant slopes or intercepts across the percentage of species removed.
If model = "BM", only plots 1, 2 and 4 are printed. Plot 3, phylogenetic model parameter is not available for model = "BM"
Gustavo Paterno
ggplot
, samp_phylm
samp_phyglm
sensi_plot.sensiSamp.TraitEvol
Plot results from samp_continuous
and
samp_discrete
## S3 method for class 'sensiSamp.TraitEvol' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiSamp.TraitEvol' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
... |
further arguments to methods |
For 'x' from samp_continuous or samp_discrete:
Graph 1: Estimated q12 (discrete) or sigsq (discrete) for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full model (blue = lower than 5, orange = between 5 and 10 and red = higher than 10).The red horizontal line represents the original value from the full model (with all species).
Graph 2: The proportion of estimated q12 (discrete) or sigsq (discrete) in each category across the percentage of species removed.
Graph 3: Estimated q21 for each simulation across the percentage of species removed
(only for samp_discrete
).
Graph 4: The percentage of significant q21 across the
percentage of species removed (only for samp_discrete
).
Gustavo Paterno & Gijsbert Werner
ggplot
, samp_continuous
samp_discrete
plot_tree.intra_phylm
Plot results from tree_phylm
,
intra_phylm
and intra_phyglm
## S3 method for class 'sensiTree' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiTree' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1, 2 or 3) |
... |
further arguments to methods |
For 'x' from tree_phylm
, tree_phyglm
,
intra_phylm
or intra_phyglm
:
Graphs 1 and 2: Distribution of estimated estimates and intercepts for each tree (for tree_phylm
) or
value generated within a given interval (intra_phylm
)
Red vertical line represents the mean estimate or intercept for all models.
Graph 3: Scatterplot with mean regression (black line) and standard deviation of the regression (blue dotted lines).
Caterina Penone and Gustavo Paterno
ggplot
, tree_phylm
intra_phylm
Plot results from tree_clade_phylm
and tree_clade_phyglm
## S3 method for class 'sensiTree_Clade' sensi_plot(x, clade = NULL, graphs = "all", ...)
## S3 method for class 'sensiTree_Clade' sensi_plot(x, clade = NULL, graphs = "all", ...)
x |
output from |
clade |
The name of the clade to be evaluated (see details) |
graphs |
Choose which graph should be printed on the output ("all", 1 or 2). Defaults to "all". |
... |
further arguments to methods. |
For 'x' from tree_clade_phylm or tree_clade_phyglm:
Graph 1: Estimated slopes after clade removal (reduced data) across multiple trees. Small dots represent estimates reruns between phylogenetic trees while larger dots represents the average estimate between all trees for each clade. The solid black line represents the average slope estimate among trees using the full dataset.
Graph 2: The effect of clade removal on slope estimate across all individual phylogenetic trees for each clade analyzed. The black line indicates estimates with the full dataset while the red line represent estimates without the focal clade (reduced data) across different trees. The blue dots represent null expectation estimates after removing the same number of species of the focal clade, with dots falling outside the red line area indicating a larger than expected absolute effect.
Gustavo Paterno, Caterina Penone
sensi_plot.sensiTree_Influ
Plot results from tree_influ_phylm
and
tree_influ_phyglm
## S3 method for class 'sensiTree_Influ' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiTree_Influ' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
... |
further arguments to methods |
For 'x' from sensiTree_Influ_phylm or sensiTree_Influ_phyglm:
Graph 1: Most common influential species on model estimates. Percentage of iterations (n.tree or n.intra) where the removal of individual species caused significant change in model estimate (sDIFestimate > cutoff).
Graph 2: Shift on model estimate after species removal for most influential species. Small red dots represent individual reruns between different trees or simulations while large red dots represent the average DIFestimate among all iterations.
Gustavo Paterno, Caterina Penone
plot_tree.intra_phylm
Plot results from tree_intra_phylm
,
tree_intra_phylm
and tree_intra_phyglm
## S3 method for class 'sensiTree_Intra' sensi_plot(x, graphs = "all", uncer.type = "all", ...)
## S3 method for class 'sensiTree_Intra' sensi_plot(x, graphs = "all", uncer.type = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1, 2 or 3) |
uncer.type |
choose which uncertainty type should be printed ("all", "intra", "tree") |
... |
further arguments to methods |
For 'x' from tree_intra_phylm
or tree_intra_phyglm
:
Graphs 1 and 2: Distribution of estimated estimates and intercepts for each tree (for tree_phylm
) or
value generated within a given interval (interaction_intra_tree_phylm
)
Red vertical line represents the mean estimate or intercept for all models.
Graph 3: Scatterplot with mean regression (black line) and standard deviation of the regression (blue dotted lines).
Caterina Penone and Gustavo Paterno
ggplot
, tree_phylm
intra_phylm
sensi_plot.sensiTree_Samp
Plot results from sensiTree_Samp_phylm
and
sensiTree_Samp_phyglm
## S3 method for class 'sensiTree_Samp' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiTree_Samp' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed on the output ("all", 1,2,3 or 4) |
... |
further arguments to methods |
For 'x' from sensiTree_Samp_phylm or sensiTree_Samp_phyglm:
Graph 1: : Estimated estimates for each simulation across percentages of species removed. Small red dots represent individual estimates for each iteration (tree or intra) across percentages of removed species. Large dots represent the average among these estimates within each iteration and percentage of species removal. Different estimates within the same percentage interval represent different iterations (tree or intra).
Graph 2: : The percentage of significant estimates (p < 0.05) across the percentage of species removed. Small red dots represent individual iterations (tree or intra) while large dots represent the average among these iterations.
If model = "BM", only plots 1, 2 and 4 are printed. Plot 3, phylogenetic model parameter is not available for model = "BM"
Gustavo Paterno
ggplot
, samp_phylm
samp_phyglm
sensi_plot.sensiTree.TraitEvol
Plot results from tree_discrete
and tree_continuous
.
## S3 method for class 'sensiTree.TraitEvol' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'sensiTree.TraitEvol' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", "q12", "q21", "aic" or" "optpar") |
... |
further arguments to methods |
Gijsbert Werner
ggplot
, tree_discrete
tree_continuous
The following graphs are printed.
Graph aicc: Distribution of estimated AICc-values across each tree. Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
Graph optpar: Distribution of estimated values for optimisation parameter specified using 'transform' (if applicable) Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
Additionally, only for tree_discrete
the function creates the following graphs.
Graph q12: Distribution of estimated parameter values for transition rates q12 for each tree. Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
Graph q21: Distribution of estimated parameter values for transition rates q21 for each tree. Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
While only for tree_continuous
the function creates the following graphs.
Graph sigsq: Distribution of estimated parameter values for rate of evolution sigsq for each tree. Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
Graph z0: Distribution of estimated parameter values for z0 for each tree. Red vertical line represents the mean value among all estimates. Blue vertical line represents the median value among all estimates.
sensi_plot_tree.bd
Plot results from tree_bd
.
## S3 method for class 'tree.bd' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'tree.bd' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1 or 2) |
... |
further arguments to methods |
For 'x' from tree_bd
Graphs 1: Distribution of estimated diversification or speciation rate for each tree. Red vertical line represents the average signal among all estimates.
Graphs 2: Estimates across each phylogenetic tree.
Caterina Penone and Gustavo Paterno
ggplot
, tree_phylm
intra_phylm
sensi_plot_tree.physig
Plot results from tree_physig
.
## S3 method for class 'tree.physig' sensi_plot(x, graphs = "all", ...)
## S3 method for class 'tree.physig' sensi_plot(x, graphs = "all", ...)
x |
output from |
graphs |
choose which graph should be printed in the output ("all", 1 or 2) |
... |
further arguments to methods |
For 'x' from tree_physig
Graphs 1: Distribution of estimated phylogenetic signal (lambda or K) for each tree Red vertical line represents the average signal among all estimates.
Graph 2: Distribution of p-values for the phylogenetic signal (K or lambda) for each tree. Red vertical line represents the alpha significance level = 0.05.
Caterina Penone and Gustavo Paterno
ggplot
, tree_phylm
intra_phylm
An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error).
https://github.com/paternogbc/sensiPhy/issues.
You can find instructions on how to contribute to sensiPhy at this link: https://github.com/paternogbc/sensiPhy/wiki/How-to-support-sensiPhy
A quick tutorial is available at this link: https://github.com/paternogbc/sensiPhy/wiki
Gustavo Paterno; Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.
Performs estimates of diversification rate evaluating uncertainty in trees topology.
tree_bd(phy, n.tree = "all", method = "ms", track = F, ...)
tree_bd(phy, n.tree = "all", method = "ms", track = F, ...)
phy |
A phylogeny (class 'multiPhylo', see ? |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file. (If |
method |
the method for estimating diversification rate ("ms" or "km") (see Details). |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function estimates net diversification rate using bd.ms
(Magallon and Sanderson (2000) method) or speciation rate using bd.km
(Kendall-Moran method) for n trees, randomly picked from a multiPhylo file.
Output can be visualised using sensi_plot
.
The function tree_bd
returns a list with the following
components:
tree.bd.estimates
: Three number, diversification/speciation rate estimate
("Magallon and Sanderson" or "Kendall-Moran") for each run with a different phylogenetic tree.
stats
: Main statistics for estimates across trees.CI_low
and CI_high
are the lower
and upper limits of the 95
Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Magallon S and MJ Sanderson. 2000. Absolute diversification rates in angiosperm clades. Evolution 55:1762-1780.
data("primates") # To estimate diversification rate with Magallon and Sanderson method: fit <- tree_bd(phy = primates.phy, n.tree = 30, method = "ms") summary(fit) sensi_plot(fit) # To estimate speciation rate Kendall-Moran method fit <- tree_bd(phy = primates.phy, n.tree = 30, method = "km") summary(fit) sensi_plot(fit)
data("primates") # To estimate diversification rate with Magallon and Sanderson method: fit <- tree_bd(phy = primates.phy, n.tree = 30, method = "ms") summary(fit) sensi_plot(fit) # To estimate speciation rate Kendall-Moran method fit <- tree_bd(phy = primates.phy, n.tree = 30, method = "km") summary(fit) sensi_plot(fit)
Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis and evaluating uncertainty in trees topology.
tree_clade_phyglm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.tree = 2, btol = 50, track = TRUE, ... )
tree_clade_phyglm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.tree = 2, btol = 50, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'multiPhylo', see ? |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
Currently only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
This function sequentially removes one clade at a time, fits a phylogenetic
logistic regression model using phyloglm
and stores the
results. The impact of of a specific clade on model estimates is calculated by the
comparison between the full model (with all species) and the model without
the species belonging to a clade. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution of slopes expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
clade_phyglm
detects influential clades based on
difference in intercept and/or slope when removing a given clade compared
to the full model including all species. This is done for n trees in the multiphylo file.
Currently, this function can only implements simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function clade_phyglm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for a tree iteration. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used)
are reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades and/or trees where deletion resulted in errors.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, tree_phyglm
,
clade_phyglm
, tree_clade_phylm
,
sensi_plot
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(150, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) cla <- rep(c("A","B","C","D","E"), each = 30) dat = data.frame(y, x, cla) # Run sensitivity analysis: tree_clade <- tree_clade_phyglm(y ~ x, phy = mphy, data = dat, n.tree = 10, n.sim = 10, clade.col = "cla") # To check summary results and most influential clades: summary(tree_clade) # Visual diagnostics for clade removal: sensi_plot(tree_clade) # Specify which clade removal to plot: sensi_plot(tree_clade, "B") sensi_plot(tree_clade, "C", graphs = 2) sensi_plot(tree_clade, "D", graphs = 2) ## End(Not run)
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(150, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) cla <- rep(c("A","B","C","D","E"), each = 30) dat = data.frame(y, x, cla) # Run sensitivity analysis: tree_clade <- tree_clade_phyglm(y ~ x, phy = mphy, data = dat, n.tree = 10, n.sim = 10, clade.col = "cla") # To check summary results and most influential clades: summary(tree_clade) # Visual diagnostics for clade removal: sensi_plot(tree_clade) # Specify which clade removal to plot: sensi_plot(tree_clade, "B") sensi_plot(tree_clade, "C", graphs = 2) sensi_plot(tree_clade, "D", graphs = 2) ## End(Not run)
Estimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis and evaluating uncertainty in trees topology.
tree_clade_phylm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.tree = 2, model = "lambda", track = TRUE, ... )
tree_clade_phylm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.tree = 2, model = "lambda", track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'multiPhylo', see ? |
clade.col |
The column in the provided data frame which specifies the clades (a character vector with clade names). |
n.species |
Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
n.sim |
Number of simulations for the randomization test. |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one clade at a time, fits a phylogenetic
linear regression model using phylolm
and stores the
results. The impact of of a specific clade on model estimates is calculated by the
comparison between the full model (with all species) and the model without
the species belonging to a clade. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution of slopes expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
clade_phylm
detects influential clades based on
difference in intercept and/or slope when removing a given clade compared
to the full model including all species. This is done for n trees in the multiphylo file.
Currently, this function can only implement simple linear models (i.e.
). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function clade_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for a tree iteration. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the percentage of change
in intercept compared to the full model (intercept.perc
) and intercept
p-value (pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used)
are reported.
null.dist
: A data frame with estimates for the null distributions
for all clades analysed.
data
: Original full dataset.
errors
: Clades and/or trees where deletion resulted in errors.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, tree_phylm
,
clade_phylm
, tree_clade_phyglm
,
sensi_plot
## Not run: # Load data: data(primates) # run analysis: clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy, data = primates$data, clade.col = "family", n.sim = 50, n.tree = 5) # To check summary results and most influential clades: summary(clade_tree) # Visual diagnostics for clade removal: sensi_plot(clade_tree) # Specify which clade removal to plot: sensi_plot(clade_tree) sensi_plot(clade_tree, "Cercopithecidae") sensi_plot(clade_tree, clade = "Cebidae", graphs = 2) ## End(Not run)
## Not run: # Load data: data(primates) # run analysis: clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy, data = primates$data, clade.col = "family", n.sim = 50, n.tree = 5) # To check summary results and most influential clades: summary(clade_tree) # Visual diagnostics for clade removal: sensi_plot(clade_tree) # Specify which clade removal to plot: sensi_plot(clade_tree) sensi_plot(clade_tree, "Cercopithecidae") sensi_plot(clade_tree, clade = "Cebidae", graphs = 2) ## End(Not run)
Fits models for trait evolution of continuous characters, evaluating phylogenetic uncertainty.
tree_continuous( data, phy, n.tree = 10, model, bounds = list(), n.cores = NULL, track = TRUE, ... )
tree_continuous( data, phy, n.tree = 10, model, bounds = list(), n.cores = NULL, track = TRUE, ... )
data |
Data vector for a single continuous trait, with names matching tips in |
phy |
Phylogenies (class 'multiPhylo', see ? |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file. If NULL, |
model |
The evolutionary model (see Details). |
bounds |
settings to constrain parameter estimates. See |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits different models of continuous character evolution using fitContinuous
to n trees, randomly picked in a multiPhylo file.
Different evolutionary models from fitContinuous
can be used, i.e. BM
,OU
,
EB
, trend
, lambda
, kappa
, delta
and drift
.
See fitContinuous
for more details on character models and tree transformations.
Output can be visualised using sensi_plot
.
The function tree_continuous
returns a list with the following
components:
call
: The function call
data
: The original full data vector
sensi.estimates
: (rate of evolution sigsq
,
root state z0
and where applicable optpar
),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for each analysis with a different phylogenetic tree.
N.tree
: Number of trees n.tree
analysed
stats
: Main statistics for model parameters, i.e. minimum, maximum, mean, median and sd-values
optpar
: Evolutionary model used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: #Load data: data("primates") #Model trait evolution accounting for phylogenetic uncertainty adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) tree_cont<-tree_continuous(data = adultMass,phy = primates$phy, model = "OU",n.tree=30,n.cores = 2,track = TRUE) #Print summary statistics summary(tree_cont) sensi_plot(tree_cont) sensi_plot(tree_cont,graphs="sigsq") sensi_plot(tree_cont,graphs="optpar") #Use a different evolutionary model tree_cont2<-tree_continuous(data = adultMass,phy = primates$phy, model = "delta",n.tree=30,n.cores = 2,track = TRUE) summary(tree_cont2) sensi_plot(tree_cont2) ## End(Not run)
## Not run: #Load data: data("primates") #Model trait evolution accounting for phylogenetic uncertainty adultMass<-primates$data$adultMass names(adultMass)<-rownames(primates$data) tree_cont<-tree_continuous(data = adultMass,phy = primates$phy, model = "OU",n.tree=30,n.cores = 2,track = TRUE) #Print summary statistics summary(tree_cont) sensi_plot(tree_cont) sensi_plot(tree_cont,graphs="sigsq") sensi_plot(tree_cont,graphs="optpar") #Use a different evolutionary model tree_cont2<-tree_continuous(data = adultMass,phy = primates$phy, model = "delta",n.tree=30,n.cores = 2,track = TRUE) summary(tree_cont2) sensi_plot(tree_cont2) ## End(Not run)
Fits models for trait evolution of discrete (binary) characters, evaluating phylogenetic uncertainty.
tree_discrete( data, phy, n.tree = 10, model, transform = "none", bounds = list(), n.cores = NULL, track = TRUE, ... )
tree_discrete( data, phy, n.tree = 10, model, transform = "none", bounds = list(), n.cores = NULL, track = TRUE, ... )
data |
Data vector for a single binary trait, with names matching tips in |
phy |
Phylogenies (class 'multiPhylo', see ? |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file. If NULL, |
model |
The Mkn model to use (see Details). |
transform |
The evolutionary model to transform the tree (see Details). Default is |
bounds |
settings to constrain parameter estimates. See |
n.cores |
number of cores to use. If 'NULL', number of cores is detected. |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits different models of discrete character evolution using fitDiscrete
to n trees, randomly picked in a multiPhylo file. Currently, only binary discrete traits are supported
Different character model from fitDiscrete
can be used, including ER
(equal-rates),
SYM
(symmetric), ARD
(all-rates-different) and meristic
(stepwise fashion).
All transformations to the phylogenetic tree from fitDiscrete
can be used, i.e. none
,
EB
, lambda
, kappa
anddelta
.
See fitDiscrete
for more details on character models and tree transformations.
Output can be visualised using sensi_plot
.
The function tree_discrete
returns a list with the following
components:
call
: The function call
data
: The original full data vector
sensi.estimates
: Parameter estimates (transition rates q12 and q21),
AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda
)
for each analysis with a different phylogenetic tree.
N.tree
: Number of trees n.tree
analysed
stats
: Main statistics for model parameters, i.e. minimum, maximum, mean, median and sd-values
optpar
: Transformation parameter used (e.g. lambda
, kappa
etc.)
Gijsbert Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for phylogenetic uncertainty tree_binary<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "ARD",transform = "none",n.tree = 30,n.cores = 2,track = TRUE) #Print summary statistics summary(tree_binary) sensi_plot(tree_binary) sensi_plot(tree_binary,graphs="q12") sensi_plot(tree_binary,graphs="q21") #Use a different evolutionary model or transformation. tree_binary_lambda<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "lambda",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_lambda) #Using Pagel's Lambda sensi_plot(tree_binary_lambda) #Symmetrical rates, with an Early Burst (EB) model of trait evolution tree_binary_SYM_EB<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "EB",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB,graphs="optpar") ## End(Not run)
## Not run: #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for phylogenetic uncertainty tree_binary<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "ARD",transform = "none",n.tree = 30,n.cores = 2,track = TRUE) #Print summary statistics summary(tree_binary) sensi_plot(tree_binary) sensi_plot(tree_binary,graphs="q12") sensi_plot(tree_binary,graphs="q21") #Use a different evolutionary model or transformation. tree_binary_lambda<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "lambda",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_lambda) #Using Pagel's Lambda sensi_plot(tree_binary_lambda) #Symmetrical rates, with an Early Burst (EB) model of trait evolution tree_binary_SYM_EB<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "EB",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB,graphs="optpar") ## End(Not run)
Performs leave-one-out deletion analysis for phylogenetic logistic regression, and detects influential species while evaluating uncertainty in trees topology.
tree_influ_phyglm( formula, data, phy, n.tree = 2, cutoff = 2, btol = 50, track = TRUE, ... )
tree_influ_phyglm( formula, data, phy, n.tree = 2, cutoff = 2, btol = 50, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.tree |
Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. |
cutoff |
The cutoff value used to identify for influential species (see Details) |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time, fits a phylogenetic
logistic regression model using phyloglm
, stores the
results and detects influential species. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
Currently only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
influ_phyglm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function influ_phyglm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (i.e. alpha
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in interecept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for a given random tree. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the standardised
difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(i.e. alpha
) are reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, tree_phyglm
,
influ_phyglm
, tree_influ_phyglm
, sensi_plot
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(100, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree_influ <- tree_influ_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 5) summary(tree_influ) sensi_plot(tree_influ) sensi_plot(tree_influ, graphs = 1) sensi_plot(tree_influ, graphs = 2) ## End(Not run)
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(100, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree_influ <- tree_influ_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 5) summary(tree_influ) sensi_plot(tree_influ) sensi_plot(tree_influ, graphs = 1) sensi_plot(tree_influ, graphs = 2) ## End(Not run)
Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species while evaluating uncertainty in trees topology.
tree_influ_phylm( formula, data, phy, n.tree = 2, cutoff = 2, model = "lambda", track = TRUE, ... )
tree_influ_phylm( formula, data, phy, n.tree = 2, cutoff = 2, model = "lambda", track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.tree |
Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. |
cutoff |
The cutoff value used to identify for influential species (see Details) |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function sequentially removes one species at a time, fits a phylogenetic
linear regression model using phylolm
, stores the
results and detects influential species. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
influ_phylm
detects influential species based on the standardised
difference in intercept and/or slope when removing a given species compared
to the full model including all species. Species with a standardised difference
above the value of cutoff
are identified as influential. The default
value for the cutoff is 2 standardised differences change.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function influ_phylm
returns a list with the following
components:
cutoff
: The value selected for cutoff
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for the full model
without deleted species.
influential_species
: List of influential species, both
based on standardised difference in intercept and in the slope of the
regression. Species are ordered from most influential to less influential and
only include species with a standardised difference > cutoff
.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a deleted clade for a given random tree. Columns report the calculated
regression intercept (intercept
), difference between simulation
intercept and full model intercept (DIFintercept
), the standardised
difference (sDIFintercept
), the percentage of change in intercept compared
to the full model (intercept.perc
) and intercept p-value
(pval.intercept
). All these parameters are also reported for the regression
slope (DIFestimate
etc.). Additionally, model aic value (AIC
) and
the optimised value (optpar
) of the phylogenetic parameter
(e.g. kappa
or lambda
, depending on the phylogenetic model used) are
reported.
data
: Original full dataset.
errors
: Species where deletion resulted in errors.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, tree_phylm
,
influ_phylm
, tree_influ_phyglm
, sensi_plot
## Not run: # Load data: data(alien) # run analysis: tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 5) # To check summary results: summary(tree_influ) # Visual diagnostics sensi_plot(tree_influ) sensi_plot(tree_influ, graphs = 1) sensi_plot(tree_influ, graphs = 2) data(alien) tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data[1:25, ], n.tree = 2) summary(tree_influ) ## End(Not run)
## Not run: # Load data: data(alien) # run analysis: tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 5) # To check summary results: summary(tree_influ) # Visual diagnostics sensi_plot(tree_influ) sensi_plot(tree_influ, graphs = 1) sensi_plot(tree_influ, graphs = 2) data(alien) tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data[1:25, ], n.tree = 2) summary(tree_influ) ## End(Not run)
Performs Phylogenetic logistic regression evaluating intraspecific variability in response and/or predictor variables and uncertainty in trees topology.
tree_intra_phyglm( formula, data, phy, Vx = NULL, x.transf = NULL, n.intra = 10, n.tree = 2, distrib = "normal", track = TRUE, btol = 50, ... )
tree_intra_phyglm( formula, data, phy, Vx = NULL, x.transf = NULL, n.intra = 10, n.tree = 2, distrib = "normal", track = TRUE, btol = 50, ... )
formula |
The model formula: |
data |
Data frame containing species traits and species names as row names. |
phy |
A phylogeny (class 'phylo', see ? |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
x.transf |
Transformation for the predictor variable (e.g. |
n.intra |
Number of times to repeat the analysis generating a random value for response and/or predictor variables.
If NULL, |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
track |
Print a report tracking function progress (default = TRUE) |
btol |
Bound on searching space. For details see |
... |
Further arguments to be passed to |
This function fits a phylogenetic logistic regression model using phyloglm
to n trees (n.tree
),
randomly picked in a multiPhylo file. The regression is also repeated n.intra
times.
At each iteration the function generates a random value for each row in the dataset using the standard deviation
or errors supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data,
you can use the summarySE
function from the package Rmisc
.
#' All phylogenetic models from phyloglm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phyloglm
for details.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function tree_intra_phylm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised value of the phylogenetic
parameter (e.g. lambda
) for each regression using a value in the interval of variation and
a different phylogenetic tree.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters.
Fields coded using all
describe statistics due to both intraspecific variation and phylogenetic uncertainty.
Fields coded using intra
describe statistics due to intraspecific variation only.
Fields coded using tree
describe statistics due to phylogenetic uncertainty only.
sd
is the standard deviation. CI_low
and CI_high
are the lower and upper limits
of the 95
sp.pb
: Species that caused problems with data transformation (see details above).
When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing times
, changing the transformation type and/or checking the target species in output$sp.pb.
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, tree_phyglm
, intra_phyglm
,
tree_intra_phylm
, sensi_plot
# Simulate data set.seed(6987) mphy = ape::rmtree(150, N = 30) x = phylolm::rTrait(n=1,phy=mphy[[1]]) x_sd = rnorm(150,mean = 0.8,sd=0.2) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x, x_sd) intra.tree <- tree_intra_phyglm(y ~ x, data = dat, phy = mphy, n.intra = 3, n.tree = 3, Vx = "x_sd") # summary results: summary(intra.tree) # Visual diagnostics for phylogenetic uncertainty: sensi_plot(intra.tree, uncer.type = "all") #or uncer.type = "tree", uncer.type = "intra"
# Simulate data set.seed(6987) mphy = ape::rmtree(150, N = 30) x = phylolm::rTrait(n=1,phy=mphy[[1]]) x_sd = rnorm(150,mean = 0.8,sd=0.2) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x, x_sd) intra.tree <- tree_intra_phyglm(y ~ x, data = dat, phy = mphy, n.intra = 3, n.tree = 3, Vx = "x_sd") # summary results: summary(intra.tree) # Visual diagnostics for phylogenetic uncertainty: sensi_plot(intra.tree, uncer.type = "all") #or uncer.type = "tree", uncer.type = "intra"
Performs Phylogenetic linear regression evaluating intraspecific variability in response and/or predictor variables and uncertainty in trees topology.
tree_intra_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 10, n.tree = 2, distrib = "normal", model = "lambda", track = TRUE, ... )
tree_intra_phylm( formula, data, phy, Vy = NULL, Vx = NULL, y.transf = NULL, x.transf = NULL, n.intra = 10, n.tree = 2, distrib = "normal", model = "lambda", track = TRUE, ... )
formula |
The model formula: |
data |
Data frame containing species traits and species names as row names. |
phy |
A phylogeny (class 'phylo', see ? |
Vy |
Name of the column containing the standard deviation or the standard error of the response
variable. When information is not available for one taxon, the value can be 0 or |
Vx |
Name of the column containing the standard deviation or the standard error of the predictor
variable. When information is not available for one taxon, the value can be 0 or |
y.transf |
Transformation for the response variable (e.g. |
x.transf |
Transformation for the predictor variable (e.g. |
n.intra |
Number of times to repeat the analysis generating a random value for response and/or predictor variables.
If NULL, |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
distrib |
A character string indicating which distribution to use to generate a random value for the response
and/or predictor variables. Default is normal distribution: "normal" (function |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic linear regression model using phylolm
to n trees (n.tree
),
randomly picked in a multiPhylo file. The regression is also repeated n.intra
times.
At each iteration the function generates a random value for each row in the dataset using the standard deviation
or errors supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data,
you can use the summarySE
function from the package Rmisc
.
#' All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function tree_intra_phylm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised value of the phylogenetic
parameter (e.g. lambda
) for each regression using a value in the interval of variation and
a different phylogenetic tree.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters.
Fields coded using all
describe statistics due to both intraspecific variation and phylogenetic uncertainty.
Fields coded using intra
describe statistics due to intraspecific variation only.
Fields coded using tree
describe statistics due to phylogenetic uncertainty only.
sd
is the standard deviation. CI_low
and CI_high
are the lower and upper limits
of the 95
sp.pb
: Species that caused problems with data transformation (see details above).
When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems
for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can
be solved by increasing times
, changing the transformation type and/or checking the target species in output$sp.pb.
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, tree_phylm
, intra_phylm
,
tree_intra_phyglm
, sensi_plot
# Load data: data(alien) # run PGLS accounting for intraspecific and phylogenetic variation: intra.tree <- tree_intra_phylm(gestaLen ~ adultMass, data = alien$data, phy = alien$phy, Vy = "SD_gesta", n.intra = 3, n.tree = 3, y.transf = log, x.transf = log) # To check summary results: summary(intra.tree) # Visual diagnostics sensi_plot(intra.tree, uncer.type = "all") #or uncer.type = "tree", uncer.type = "intra"
# Load data: data(alien) # run PGLS accounting for intraspecific and phylogenetic variation: intra.tree <- tree_intra_phylm(gestaLen ~ adultMass, data = alien$data, phy = alien$phy, Vy = "SD_gesta", n.intra = 3, n.tree = 3, y.transf = log, x.transf = log) # To check summary results: summary(intra.tree) # Visual diagnostics sensi_plot(intra.tree, uncer.type = "all") #or uncer.type = "tree", uncer.type = "intra"
Performs Phylogenetic logistic regression evaluating uncertainty in trees topology.
tree_phyglm(formula, data, phy, n.tree = 2, btol = 50, track = TRUE, ...)
tree_phyglm(formula, data, phy, n.tree = 2, btol = 50, track = TRUE, ...)
formula |
The model formula |
data |
Data frame containing species traits with species as row names. |
phy |
A phylogeny (class 'multiPhylo', see ? |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic logistic regression model using phyloglm
to n trees, randomly picked in a multiPhylo file.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function tree_phyglm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for each regression with a
different phylogenetic tree.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters. sd_intra
is the standard deviation
due to intraspecific variation. CI_low
and CI_high
are the lower and upper limits
of the 95
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Donoghue, M.J. & Ackerly, D.D. (1996). Phylogenetic Uncertainties and Sensitivity Analyses in Comparative Biology. Philosophical Transactions: Biological Sciences, pp. 1241-1249.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, sensi_plot
,tree_phylm
### Simulating Data: set.seed(6987) mphy = rmtree(150, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree <- tree_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 30) # summary results: summary(tree) # Visual diagnostics for phylogenetic uncertainty: sensi_plot(tree)
### Simulating Data: set.seed(6987) mphy = rmtree(150, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,150),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree <- tree_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 30) # summary results: summary(tree) # Visual diagnostics for phylogenetic uncertainty: sensi_plot(tree)
Performs Phylogenetic linear regression evaluating uncertainty in trees topology.
tree_phylm(formula, data, phy, n.tree = 2, model = "lambda", track = TRUE, ...)
tree_phylm(formula, data, phy, n.tree = 2, model = "lambda", track = TRUE, ...)
formula |
The model formula |
data |
Data frame containing species traits with species as row names. |
phy |
A phylogeny (class 'multiPhylo', see ? |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function fits a phylogenetic linear regression model using phylolm
to n trees, randomly picked in a multiPhylo file.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function tree_phylm
returns a list with the following
components:
formula
: The formula
data
: Original full dataset
sensi.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
) for each regression with a
different phylogenetic tree.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for model parameters.CI_low
and CI_high
are the lower
and upper limits of the 95
all.stats
: Complete statistics for model parameters. sd_intra
is the standard deviation
due to intraspecific variation. CI_low
and CI_high
are the lower and upper limits
of the 95
Caterina Penone & Pablo Ariel Martinez
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Donoghue, M.J. & Ackerly, D.D. (1996). Phylogenetic Uncertainties and Sensitivity Analyses in Comparative Biology. Philosophical Transactions: Biological Sciences, pp. 1241-1249.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, sensi_plot
, tree_phyglm
# Load data: data(alien) # This analysis needs a multiphylo file: class(alien$phy) alien$phy # run PGLS accounting for phylogenetic uncertain: tree <- tree_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 30) # To check summary results: summary(tree) # Visual diagnostics sensi_plot(tree) # You can specify which graph to print: sensi_plot(tree, graphs = 3)
# Load data: data(alien) # This analysis needs a multiphylo file: class(alien$phy) alien$phy # run PGLS accounting for phylogenetic uncertain: tree <- tree_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 30) # To check summary results: summary(tree) # Visual diagnostics sensi_plot(tree) # You can specify which graph to print: sensi_plot(tree, graphs = 3)
Performs phylogenetic signal estimates evaluating uncertainty in trees topology.
tree_physig( trait.col, data, phy, n.tree = "all", method = "K", track = TRUE, ... )
tree_physig( trait.col, data, phy, n.tree = "all", method = "K", track = TRUE, ... )
trait.col |
The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass"). |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.tree |
Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file. (If |
method |
Method to compute signal: can be "K" or "lambda". |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function estimates phylogenetic signal using phylosig
to n trees, randomly picked in a multiPhylo file.
Output can be visualised using sensi_plot
.
The function tree_physig
returns a list with the following
components:
Trait
: Column name of the trait analysed
data
: Original full dataset
tree.physig.estimates
: Three number, phylogenetic signal estimate
(lambda or K) and the p-value for each run with a different phylogenetic tree.
N.obs
: Size of the dataset after matching it with tree tips and removing NA's.
stats
: Main statistics for phylogenetic estimates.CI_low
and CI_high
are the lower
and upper limits of the 95
The argument "se" from phylosig
is not available in this function. Use the
argument "V" instead with intra_physig
to indicate the name of the column containing the standard
deviation or the standard error of the trait variable instead.
Caterina Penone & Gustavo Paterno
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Donoghue, M.J. & Ackerly, D.D. (1996). Phylogenetic Uncertainties and Sensitivity Analyses in Comparative Biology. Philosophical Transactions: Biological Sciences, pp. 1241-1249.
Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.
Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.
Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.
phylosig
,
tree_phylm
,sensi_plot
# Load data: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: tree <- tree_physig(trait.col = "logMass", data = alien.data, phy = alien.phy, n.tree = 10) summary(tree) sensi_plot(tree) sensi_plot(tree, graphs = 1) sensi_plot(tree, graphs = 2)
# Load data: data(alien) alien.data<-alien$data alien.phy<-alien$phy # Logtransform data alien.data$logMass <- log(alien.data$adultMass) # Run sensitivity analysis: tree <- tree_physig(trait.col = "logMass", data = alien.data, phy = alien.phy, n.tree = 10) summary(tree) sensi_plot(tree) sensi_plot(tree, graphs = 1) sensi_plot(tree, graphs = 2)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in phylogenetic logistic regression, while evaluating uncertainty in trees topology.
tree_samp_phyglm( formula, data, phy, n.sim = 30, n.tree = 2, breaks = seq(0.1, 0.5, 0.1), btol = 50, track = TRUE, ... )
tree_samp_phyglm( formula, data, phy, n.sim = 30, n.tree = 2, breaks = seq(0.1, 0.5, 0.1), btol = 50, track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
n.tree |
Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. |
breaks |
A vector containing the percentages of species to remove. |
btol |
Bound on searching space. For details see |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic logistic regression, fits a phylogenetic
logistic regression model without these species using phyloglm
,
repeats this many times (controlled by times
), stores the results and
calculates the effects on model parameters. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
Only logistic regression using the "logistic_MPLE"-method from
phyloglm
is implemented.
Currently, this function can only implement simple logistic models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.
Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
#' Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phyloglm
, samp_phyglm
,
tree_phyglm
, tree_samp_phylm
,sensi_plot
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(100, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree_samp <- tree_samp_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 3, n.sim=10) summary(tree_samp) sensi_plot(tree_samp) sensi_plot(tree_samp, graphs = 1) sensi_plot(tree_samp, graphs = 2) ## End(Not run)
## Not run: # Simulate Data: set.seed(6987) mphy = rmtree(100, N = 30) x = rTrait(n=1,phy=mphy[[1]]) X = cbind(rep(1,100),x) y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X) dat = data.frame(y, x) # Run sensitivity analysis: tree_samp <- tree_samp_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 3, n.sim=10) summary(tree_samp) sensi_plot(tree_samp) sensi_plot(tree_samp, graphs = 1) sensi_plot(tree_samp, graphs = 2) ## End(Not run)
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic linear regression, while evaluating uncertainty in trees topology.
tree_samp_phylm( formula, data, phy, n.sim = 30, n.tree = 2, breaks = seq(0.1, 0.5, 0.1), model = "lambda", track = TRUE, ... )
tree_samp_phylm( formula, data, phy, n.sim = 30, n.tree = 2, breaks = seq(0.1, 0.5, 0.1), model = "lambda", track = TRUE, ... )
formula |
The model formula |
data |
Data frame containing species traits with row names matching tips
in |
phy |
A phylogeny (class 'phylo') matching |
n.sim |
The number of times species are randomly deleted for each
|
n.tree |
Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. |
breaks |
A vector containing the percentages of species to remove. |
model |
The phylogenetic model to use (see Details). Default is |
track |
Print a report tracking function progress (default = TRUE) |
... |
Further arguments to be passed to |
This function randomly removes a given percentage of species (controlled by
breaks
) from the full phylogenetic linear regression, fits a phylogenetic
linear regression model without these species using phylolm
,
repeats this many times (controlled by n.sim
), stores the results and
calculates the effects on model parameters. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
All phylogenetic models from phylolm
can be used, i.e. BM
,
OUfixedRoot
, OUrandomRoot
, lambda
, kappa
,
delta
, EB
and trend
. See ?phylolm
for details.
Currently, this function can only implement simple linear models (i.e. ). In the future we will implement more complex models.
Output can be visualised using sensi_plot
.
The function samp_phylm
returns a list with the following
components:
formula
: The formula
full.model.estimates
: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g. lambda
or kappa
) for
the full model without deleted species.
sensi.estimates
: A data frame with all simulation
estimates. Each row represents a model rerun with a given number of species
n.remov
removed, representing n.percent
of the full dataset.
Columns report the calculated regression intercept (intercept
),
difference between simulation intercept and full model intercept (DIFintercept
),
the percentage of change in intercept compared to the full model (intercept.perc
)
and intercept p-value (pval.intercept
). All these parameters are also reported
for the regression slope (DIFestimate
etc.). Additionally, model aic value
(AIC
) and the optimised value (optpar
) of the phylogenetic
parameter (e.g. kappa
or lambda
, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
(sDIFintercept
) and slope (sDIFestimate
).
sign.analysis
For each break (i.e. each percentage of species
removed) this reports the percentage of statistically significant (at p<0.05)
intercepts (perc.sign.intercept
) over all repetitions as well as the
percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate
).
data
: Original full dataset.
#' @note Please be aware that dropping species may reduce power to detect
significant slopes/intercepts and may partially be responsible for a potential
effect of species removal on p-values. Please also consult standardised differences
in the (summary) output.
Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone
Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467
Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
phylolm
, samp_phylm
,
tree_phylm
,tree_samp_phyglm
,sensi_plot
## Not run: # Load data: data(alien) # Run analysis: samp <- tree_samp_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 5, n.sim=10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics sensi_plot(samp) sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)
## Not run: # Load data: data(alien) # Run analysis: samp <- tree_samp_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, data = alien$data, n.tree = 5, n.sim=10) summary(samp) head(samp$sensi.estimates) # Visual diagnostics sensi_plot(samp) sensi_plot(samp, graphs = 1) sensi_plot(samp, graphs = 2) ## End(Not run)