# Plots calcConv or convSig output as a two-dimensional time series. plot2D Plots calcConv or convSig output as a two-dimensional time series. ```r plot2D( Ct, phy, tip, foc.trt, trait, pca = FALSE, save = FALSE, filename = "frame", dir = NULL, leg = FALSE, leg.pos = "topleft", width = 480, height = 480, ... ) ``` ## Arguments - `Ct`: object containing calcConvCt or convSigCt output - `phy`: The time calibrated phylogeny of interest in phylo format - `tip`: vector of two tip labels indicating the putatively convergent taxa to be plotted in morphospace - `foc.trt`: vector of two traits to be used in plotting. If pca == TRUE this should indicate which PC axes should be plotted in format "PC1" - `trait`: the matrix of trait values used to compute Ct - `pca`: logical value indicating whether to conduct principal component analysis and plot PC scores instead of raw trait values - `save`: logical value indicating whether an animated time series (along with individual time slices) should be saved to a user specified folder - `filename`: character indicating the desired prefix for filenames to be saved - `dir`: optional character indicating the folderpath of the desired save location - `leg`: logical value indicating whether a legend should be added to plots - `leg.pos`: character indicating the position of the legend - `width`: pixel width of saved png files - `height`: pixel height of saved png files - `...`: optional arguments to be passed to plot ## Returns Plots tracking putatively convergent taxa in two-dimensional morphospace through time A table with trait values, morphospace distance, and nodeheights conincident with Dmax.t. See meas.path output from calcConvCt and convSigCt ## Details None ## Examples ```r ## Not run: library(phytools) library(geiger) # create time calibrated tree mytree<-rtree(100) mycalibration <- makeChronosCalib(mytree, node="root", age.max=50) phy <- chronos(mytree, lambda = 1, model = "correlated", calibration = mycalibration, control = chronos.control() ) class(phy)<-"phylo" # create three normally distributed phenotypic traits traits <- cbind(rnorm(Ntip(phy)),rnorm(Ntip(phy)),rnorm(Ntip(phy))) colnames(traits) <- c("V1","V2","V3") rownames(traits) <- phy$tip.label # select two random tips, excluding sister taxa pairs <- apply(combn(phy$tip.label,2),2,function(x) nodepath(phy, which(phy$tip.label == x[1]),which(phy$tip.label == x[2]))) nosis <- combn(phy$tip.label,2)[,unlist(lapply(pairs, function(x) length(x) > 3))] focaltaxa <- nosis[,sample(1:ncol(nosis),1)] system.time(run <- calcConvCt(phy, traits, focaltaxa)) system.time(run2 <- convSigCt(phy, traits, focaltaxa, nsim=100)) plot2D(run, phy, focaltaxa[1:2], colnames(traits)[1:2], traits) ## End(Not run) ``` ## References Grossnickle DM, Brightly WH, Weaver LN, Stanchak KE, Roston RA, Pevsner SK, Stayton CT, Polly PD, Law CJ. 2022. A cautionary note on quantitative measures of phenotypic convergence. in revision Zelditch ML, Ye J, Mitchell JS, Swiderski DL. 2017. Rare ecomorphological convergence on a complex adaptive landscape: Body size and diet mediate evolution of jaw shape in squirrels (Sciuridae). Evolution 71: 633-649 Stayton CT. 2015. The definition, recognition, and interpretation of convergent evolution and two new measures for quantifying and assessing the significance of convergence. Evolution 69(8): 2140-2153. Revell, L. J. (2012) phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol., 3, 217-223. Felsenstein, J. 1985. Phylogenies and the comparative method. American Naturalist, 125, 1-15.

plot2D function