| Title: | Comparative Methods for Phylogenetic Networks |
|---|---|
| Description: | Analyze the phenotypic evolution of species of hybrid origin on a phylogenetic network. This can detect a burst of variation at the formation of a hybrid as well as an increase or decrease in trait value at a hybridization event. Parameters are estimated by maximum likelihood, and model averaging can be done automatically. Users need to enter a comparative data set and a phylogenetic network. |
| Authors: | Dwueng-Chwuan Jhwueng [aut, cre], Brian C. O'Meara [aut] |
| Maintainer: | Dwueng-Chwuan Jhwueng <[email protected]> |
| License: | GPL (>=2) |
| Version: | 2.1.5 |
| Built: | 2024-12-16 03:29:35 UTC |
| Source: | https://github.com/bomeara/BMhyb |
Given an evonet object, and info on where the gene flow is from and to, and when this occurs, add a hybridization event. The edges things move from and to are specified by the list of descendant taxa of those edges (basically the edge is the subtending branch for the clade). You do not have to list all taxa, only those spanning the node at the end of the edge. You can enter a single taxon to have gene flow to or from a terminal branch. You also ideally will specify when the gene flow happens. This can be given as time from the root of the tree to when the event starts or time from the tip of the tree back to when the gene flow starts (but you must give one of these). If gene flow goes through an unsampled ghost intermediate, you can enter the length of time it spends there. If you do not specify any of these, flow is assumed to directly from the source to the recipient, with the time set at the start of the recent of the two branches (i.e., if flow goes from taxon A to taxon D, if D is younger the flow is assumed to happen partway up the terminal branch of A to directly connect to the start of D.
AddHybridization( phy.graph, from.clade, to.clade, time.from.root = NULL, time.from.tip = NULL, ghost.length = 0 )AddHybridization( phy.graph, from.clade, to.clade, time.from.root = NULL, time.from.tip = NULL, ghost.length = 0 )
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
from.clade |
A vector of names specifying taxa spanning the node descended from the focal edge for the start of the hybridization event |
to.clade |
A vector of names specifying taxa spanning the node descended from the focal edge for the start of the hybridization event |
time.from.root |
When the hybridization event starts, as measured from the root of the tree |
time.from.tip |
When the hybridization event starts, as measured from the tips of the tree (assumed to be coeval) |
ghost.length |
How long the hybrid genes spend in an unsampled species before arriving in their recipient |
An evonet object with the new hybridization event
Fits a BMhyb model to your data.
BMhyb( phy.graph, traits, free.parameter.names = c("sigma.sq", "mu", "SE", "bt", "vh"), confidence.points = 5000, measurement.error = 0, gamma = 0.5, do.Higham.correction = FALSE, do.Brissette.correction = FALSE, verbose = TRUE, likelihood.precision = 0.01, max.steps = 10, confidence.lnl = 2, control = list(reltol = 0.001) )BMhyb( phy.graph, traits, free.parameter.names = c("sigma.sq", "mu", "SE", "bt", "vh"), confidence.points = 5000, measurement.error = 0, gamma = 0.5, do.Higham.correction = FALSE, do.Brissette.correction = FALSE, verbose = TRUE, likelihood.precision = 0.01, max.steps = 10, confidence.lnl = 2, control = list(reltol = 0.001) )
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
traits |
A vector of trait values, with names equal to the names of taxa on the phylogeny |
free.parameter.names |
What parameters you want to optimize rather than use defaults; options are sigma.sq, mu, SE, bt, and vh |
confidence.points |
How many points to use to estimate parameter uncertainty |
measurement.error |
How much uncertainty there is in tip values; a single number is applied to all taxa, a vector is applied to the corresponding taxa |
gamma |
In a hybridization event, what proportion of the trait comes from the donating parent. 0.5 means half comes from each parent |
do.Higham.correction |
Variance-covariance matrices for this model are sometimes poorly conditioned; this is a hack to reduce the impact of that |
do.Brissette.correction |
Applies method of Brissette et al. 2007 to also try to fix matrix condition |
verbose |
If TRUE, BMhyb will chat about its progress |
likelihood.precision |
When optimizing, how much of a lnL improvement is required to restart optimization between starts |
max.steps |
The number of restarts without improvement it will attempt |
confidence.lnl |
For figuring out the confidence interval, how wide you want the confidence region to be in lnL space |
control |
List of options to pass to optim. ?optim for help. |
This takes an ape::evonet object. If all you have is a tree (an ape::phylo object), you can use CreateHybridlessEvonet() to convert the tree to an evonet object. You can then use the AddHybridization() function to add hybrid events to this object. Note that networks created in this way can, by chance, result in orders of nodes in the internal edge matrix that cause ape's reorder.phylo function to crash, which is called in many of the plot and write functions. You can still use the plot functions in this package, however.
Returns an object of class BMhybResult which contains best (a data.frame of the solution), good.region (data.frame of the points making up those in the confidence.lnl region), bad.region (all the other points sampled), phy.graph (same as what you put in), traits (same as what you put in), and free.parameter.names.
## Not run: utils::data("cichlid") result <- BMhyb(phy.graph=cichlid$phy.graph, traits=cichlid$trait, free.parameter.names=c("sigma.sq", "mu")) ## End(Not run)## Not run: utils::data("cichlid") result <- BMhyb(phy.graph=cichlid$phy.graph, traits=cichlid$trait, free.parameter.names=c("sigma.sq", "mu")) ## End(Not run)
Fits all possible BMhyb models to your data.
BMhybExhaustive( phy.graph, traits, measurement.error = 0, ncores = max(c(1, parallel::detectCores() - 1), na.rm = TRUE), ... )BMhybExhaustive( phy.graph, traits, measurement.error = 0, ncores = max(c(1, parallel::detectCores() - 1), na.rm = TRUE), ... )
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
traits |
A vector of trait values, with names equal to the names of taxa on the phylogeny |
measurement.error |
How much uncertainty there is in tip values; a single number is applied to all taxa, a vector is applied to the corresponding taxa |
ncores |
Number of cores to use. By default, uses parallel package to detect what's available and uses all but one. |
... |
All other parameters to pass to BMhyb (see ?BMhyb) |
This takes an ape::evonet object. If all you have is a tree (an ape::phylo object), you can use CreateHybridlessEvonet() to convert the tree to an evonet object. You can then use the AddHybridization() function to add hybrid events to this object. Note that networks created in this way can, by chance, result in orders of nodes in the internal edge matrix that cause ape's reorder.phylo function to crash, which is called in many of the plot and write functions. You can still use the plot functions in this package, however.
This will return a list with one model result per element: you can plot these individually (see ?hybResult). By default, these results will include the information about uncertainty. We also compute a summary table so you can see the point estimates for each model and the likelihoods. It is often advisable to average across models, weighting each by its AICc weight, so this is also done automatically. We also return the single best model as an object for convenience, though for most users, we would suggest using the model average and looking at a set of fairly good models rather than look only at the single best one: there are often others that are nearly as good.
We do not expect large AIC difference between models unless you have a really large tree, and so you may get a warning if this happens. It is likely something has gone wrong with optimization. Look at all the models and examine for outliers. This issue can come up with certain combinations of networks and parameters (even, very rarely, in Brownian motion with no hybridization), where a step in the likelihood (inverting a matrix) does not yield a numerically stable result (the matrix is poorly conditioned). The 'likelihoods' in such cases are wrong, and they can look too good or too bad. Neither is ideal, but you should especially beware cases where the 'best' model has likelihoods much below some of the other models – you will often see bad parameter estimates, too. If you get this, do not believe the results – perhaps look at models with better condition.
To try to help with this, if one or more of the models has poor condition at the maximum likelihood estimate, we report this as it having an obvious problem. It is still returned in the results and the original.summary.df objects, but it is excluded from model averaging, the summary.df, and the best.model return (though note the ModelNumber column in summary.df allowing you to get the matching model in the results list). A model not having an obvious problem does *not* mean it worked well, just that it does not exhibit one particular problematic issue. Essentially we're saying, "This model does not have a lion eating its foot" – which suggest it's not unhealthy in that way, but doesn't mean there's not a crocodile eating its hand. User beware. Plotting the confidence using the plot functions can help.
Returns a list of objects of class BMhybResult (results), a summary data frame (summary.df) with parameter estimates and weights for all models where we do not see obvious problems, a summary data frame of all the models, whether or no they seemed to fail (original.summary.df), the model averaged result weighted by AICc weights of the unproblematic models (model.average), and the best unproblematic model (best.model).
## Not run: utils::data("cichlid") traits.only <- cichlid$traits_and_SE$trait names(traits.only) <- rownames(cichlid$traits_and_SE) all.models <- BMhybExhaustive(phy.graph=cichlid$phy.graph, traits=traits.only) print(all.models$summary.df) ## End(Not run)## Not run: utils::data("cichlid") traits.only <- cichlid$traits_and_SE$trait names(traits.only) <- rownames(cichlid$traits_and_SE) all.models <- BMhybExhaustive(phy.graph=cichlid$phy.graph, traits=traits.only) print(all.models$summary.df) ## End(Not run)
A dataset containing a phylogenetic network and trait data for cichlid species
cichlidcichlid
A list with two items:
the phylogenetic network in ape::evonet format
a vector of trait data
a vector of standard error
The tree is made by doing a tree search with mitochondrial data from Kobmuller, S., N. Duftner, K. M. Sefc, M. Aibara,M. Stipacek, M. Blanc, B. Egger, and C. Sturmbauer. 2007.Reticulate phylogeny of gastropod-shell-breeding cichlids from Lake Tanganyika: the result of repeated introgressive hybridization. BMC Evolutionary Biology 7:7.
https://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-7-7
We then added hybridization events based on their cartoon Fig. 4: https://media.springernature.com/full/springer-static/image/art
Hybridization events with solid lines (coeval events) were modeled as going from the later of the source or descendant nodes.
Hybridization events with dotted lines, indicating ghost lineages, went from the MRCA of the source clade to the MRCA of the recipient taxon.
Trait data comes from fishbase.
Computes likelihood for a given network, set of traits, and parameters.
ComputeLikelihood( parameters, phy.graph, traits, measurement.error = 0, gamma = 0.5, do.Higham.correction = FALSE, do.Brissette.correction = FALSE, do.DE.correction = FALSE )ComputeLikelihood( parameters, phy.graph, traits, measurement.error = 0, gamma = 0.5, do.Higham.correction = FALSE, do.Brissette.correction = FALSE, do.DE.correction = FALSE )
parameters |
Named vector of parameter values; expected names are sigma.sq, mu, SE, bt, and vh |
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
traits |
A vector of trait values, with names equal to the names of taxa on the phylogeny |
measurement.error |
How much uncertainty there is in tip values; a single number is applied to all taxa, a vector is applied to the corresponding taxa |
gamma |
In a hybridization event, what proportion of the trait comes from the donating parent. 0.5 means half comes from each parent |
do.Higham.correction |
Variance-covariance matrices for this model are sometimes poorly conditioned; this is a hack to reduce the impact of that |
do.Brissette.correction |
Applies method of Brissette et al. 2007 to also try to fix matrix condition |
do.DE.correction |
Inspired by Mishra, Sudhanshu K. "The nearest correlation matrix problem: Solution by differential evolution method of global optimization." (2007) |
This takes an ape::evonet object. If all you have is a tree (an ape::phylo object), you can use CreateHybridlessEvonet() to convert the tree to an evonet object. You can then use the AddHybridization() function to add hybrid events to this object. Note that networks created in this way can, by chance, result in orders of nodes in the internal edge matrix that cause ape's reorder.phylo function to crash, which is called in many of the plot and write functions. You can still use the plot functions in this package, however.
Returns the negative log likelihood
Creates a variance-covariance matrix for a network and parameters.
ComputeVCV( phy.graph, sigma.sq = 1, mu = 0, bt = 1, vh = 0, SE = 0, measurement.error = 0, gamma = 0.5 )ComputeVCV( phy.graph, sigma.sq = 1, mu = 0, bt = 1, vh = 0, SE = 0, measurement.error = 0, gamma = 0.5 )
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
sigma.sq |
Value for sigma squared |
mu |
Value for state at the root |
bt |
Value for beta parameter |
vh |
Value for Vh, the variance that comes from a hybridization event |
SE |
Standard error |
measurement.error |
How much uncertainty there is in tip values; a single number is applied to all taxa, a vector is applied to the corresponding taxa |
gamma |
In a hybridization event, what proportion of the trait comes from the donating parent. 0.5 means half comes from each parent |
Returns the variance-covariance matrix
ape can already convert from evonet to igraph; the advantage of this function is that it uses the node ids from the evonet object for labels in igraph.
ConvertEvonetToIgraphWithNodeNumbers(phy.graph)ConvertEvonetToIgraphWithNodeNumbers(phy.graph)
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
An igraph network
phy <- ape::rcoal(5) phy.evo <- ape::evonet(phy, from=1, to=2) plot(phy.evo) # this is the ape plot phy.igraph <- ConvertEvonetToIgraphWithNodeNumbers(phy.evo) plot(phy.igraph)phy <- ape::rcoal(5) phy.evo <- ape::evonet(phy, from=1, to=2) plot(phy.evo) # this is the ape plot phy.igraph <- ConvertEvonetToIgraphWithNodeNumbers(phy.evo) plot(phy.igraph)
ape::evonet converts a phylo object to evonet, but requires having at least one hybridization event. This lets you convert to evonet without having a hybridization event.
CreateHybridlessEvonet(phy)CreateHybridlessEvonet(phy)
phy |
A phylo object (ape's basic tree format) |
An evonet object, suitable for passing as phy.graph into many of BMhyb's functions
For a given delta lnL, get the convex hull (blob encircling the points) for two dimensions
GetConvexHull(threshold = 2, df, height, x, y)GetConvexHull(threshold = 2, df, height, x, y)
threshold |
What value to exclude numbers worse than |
df |
The data.frame |
height |
The variable name to use for the height threshold |
x |
The first variable to look at for the hull |
y |
The second variable to look at for the hull |
Function to merge adaptive sampling sims for plotting
MergeExhaustiveForPlotting(exhaustive.object)MergeExhaustiveForPlotting(exhaustive.object)
exhaustive.object |
Return of BMhybExhaustive |
Returns a single BMhyb object with results from all models merged (use for plotting)
A dataset containing a phylogenetic network and trait data for Nicotiana species
nicotiananicotiana
A list with two items:
the phylogenetic network in ape::evonet format
a vector of trait data
The tree and data come from
Chase M.W., Knapp S., Cox A.V., Clarkson J.J., Butsko Y., Joseph J., Savolainen V., and Parokonny A.S. 2003. Molecular systematics, GISH and the origin of hybrid taxa in Nicotiana(Solanaceae). Annals of Botany 92: 107-127.
Clarkson J.J., Lim K.Y., Kovarik A., Chase M.W., Knapp S. and Leitch A.R. 2005. Long-term genome diploidization I allopolyploid Nicotiana section Repandae(Solanaceae). New Phytologist 168:241-252.
Komori T., Myers P.N., Yamada S., Kubo T., and Imaseki H. 2000. Comparative study of the Nicotiana species with respect to water deficit tolerance during early growth. Euphytica 116:121-130.
Note this aggregates the info from all the model runs and plots the MLE across all of them and the contours from all the sims
## S3 method for class 'BMhybExhaustiveResult' plot(x, ...)## S3 method for class 'BMhybExhaustiveResult' plot(x, ...)
x |
A BMhybExhaustive object (result of a BMhybExhaustive() call) |
... |
Other parameters to pass to hybResult |
Shows the plot of confidence regions with MLEs indicated (red dots) or a plot of pairs of traits together. Note that for the latter plot, it converts the sampled points to an even grid with interpolation; it sets any points with likelihood worse than ten units to just ten units worse so that you can see the colors near the area of the optimum.
## S3 method for class 'BMhybResult' plot( x, style = "univariate", focal.color = "red", inregion.color = "black", outregion.color = "gray", gradientworst.color = "black", gradientbest.color = "white", contour.color = "red", contour.threshold = 2, nrow = NULL, ... )## S3 method for class 'BMhybResult' plot( x, style = "univariate", focal.color = "red", inregion.color = "black", outregion.color = "gray", gradientworst.color = "black", gradientbest.color = "white", contour.color = "red", contour.threshold = 2, nrow = NULL, ... )
x |
A BMhyb object (result of a BMhyb() call) |
style |
Either univariate or contour |
focal.color |
Color for the point showing the maximum likelihood estimate |
inregion.color |
Color for univariate plot, points in the good region |
outregion.color |
Color for univariate plot, points in the bad region |
gradientworst.color |
Color for contour plot, color of the worst contour region |
gradientbest.color |
Color for contour plot, color of the best contour region |
contour.color |
Color showing the contour line for the best threshold |
contour.threshold |
What delta log likelihood to use for the best/worst threshold for the contour plot |
nrow |
The number of rows to plot in the grid for contour (will set it automatically if NULL) |
... |
Other arguments to pass to plot (for univariate only; the contour plot uses ggplot2) |
Print BMhybExhaustive result
## S3 method for class 'BMhybExhaustiveResult' print(x, ...)## S3 method for class 'BMhybExhaustiveResult' print(x, ...)
x |
A BMhybExhaustive object (result of a BMhybExhaustive() call) |
... |
Other arguments to pass to this function |
Print BMhyb result
## S3 method for class 'BMhybResult' print(x, ...)## S3 method for class 'BMhybResult' print(x, ...)
x |
A BMhyb object (result of a BMhyb() call) |
... |
Other arguments to pass to this function |
This uses a birth death process (TreeSim::sim.bd.taxa.age) to make a tree, then randomly adds hybridization events. The events are placed uniformly with time (not with numbers of taxa). If you use the phy.graph argument, you can pass in an existing phylogenetic network and it will add hybridization events to that; if you use a phy argument, it will add hybridization events to that. Note that currently there is no checking for multiple events between the same two branches. While hybridization events happen between taxa alive at the same instant of time, it is possible that the donor taxon later goes extinct with no descendants (other than the taxa of hybrid origin). These are basically ghost lineages, and this process (which then looks like gene flow going forward in time) is permitted if allow.ghost is TRUE.
SimulateNetwork( ntax = 100, nhybridizations = 10, birth = 1, death = 1, sample.f = 0.5, tree.height = 1, allow.ghost = FALSE, phy.graph = NULL, phy = NULL )SimulateNetwork( ntax = 100, nhybridizations = 10, birth = 1, death = 1, sample.f = 0.5, tree.height = 1, allow.ghost = FALSE, phy.graph = NULL, phy = NULL )
ntax |
How many surviving taxa to have on the tree (extinct taxa are pruned, with the exception of donors if allow.ghost=TRUE) |
nhybridizations |
How many hybridization events to have |
birth |
Birth rate (instantaneous rate) |
death |
Extinction rate (instantaneous) |
sample.f |
What fraction of taxa alive at the present to sample (resulting in the final ntax) |
tree.height |
Root to tip height of the final tree |
allow.ghost |
Allow a hybridization events from an unsampled ancestor |
phy.graph |
If not NULL, uses this network and adds hybridizations to it to reach nhybridizations in total |
phy |
If not NULL, uses this tree to create hybridization events on |
A phy.graph object with hybridizations
p <- SimulateNetwork(ntax=10 ,nhybridizations=2) plot(p)p <- SimulateNetwork(ntax=10 ,nhybridizations=2) plot(p)
For a given phylogenetic network generate tip data. Any values not specified use default values
SimulateTips( phy.graph, sigma.sq = 1, mu = 0, bt = 1, vh = 0, SE = 0, measurement.error = 0, gamma = 0.5, exclude.donors.recipients = TRUE )SimulateTips( phy.graph, sigma.sq = 1, mu = 0, bt = 1, vh = 0, SE = 0, measurement.error = 0, gamma = 0.5, exclude.donors.recipients = TRUE )
phy.graph |
An ape::evonet object (a phylogeny stored in phylo format that also includes a reticulation matrix) |
sigma.sq |
The Brownian motion wiggle rate |
mu |
The population mean (in the absence of hybridization) |
bt |
The beta value (multiplier on expected value for each hybridization event) |
vh |
The burst of variance that comes from a hybridization event |
SE |
Uniform uncertainty at the tips |
measurement.error |
Uncertainty at the tips, especially if it varies between species |
gamma |
In a hybridization event, what proportion of the trait comes from the donating parent. 0.5 means half comes from each parent |
exclude.donors.recipients |
If TRUE, do not generate for any donors or recipient placeholder taxa |
A vector of trait values
network <- SimulateNetwork(ntax=5, nhybridizations=2) tips <- SimulateTips(network, mu=1.1, bt=3, vh=1.1, SE=1)network <- SimulateNetwork(ntax=5, nhybridizations=2) tips <- SimulateTips(network, mu=1.1, bt=3, vh=1.1, SE=1)
Summarize BMhybExhaustive result
## S3 method for class 'BMhybExhaustiveResult' summary(object, ...)## S3 method for class 'BMhybExhaustiveResult' summary(object, ...)
object |
A BMhybExhaustive object (result of a BMhybExhaustive() call) |
... |
Other arguments to pass to this function |
A data.frame with summarized results
Summarize BMhyb result
## S3 method for class 'BMhybResult' summary(object, ...)## S3 method for class 'BMhybResult' summary(object, ...)
object |
A BMhyb object (result of a BMhyb() call) |
... |
Other arguments to pass to this function |
A data.frame with summarized results