do3PCA

Help for package do3PCA const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {do3PCA} Contents ProbPCA doBiplot phylProbPCA Title: Probabilistic Phylogenetic Principal Component Analysis Version: 1.0.0 Description: Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions. License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] Encoding: UTF-8 Depends: R (≥ 3.5.0), Rdimtools Imports: ape, phytools, matrixcalc, mclust, nloptr, ratematrix, stats, utils RoxygenNote: 7.3.1 NeedsCompilation: no Packaged: 2024-09-23 15:42:47 UTC; daniel Author: Daniel Caetano [aut, cre] Maintainer: Daniel Caetano <caetanods1@gmail.com> Repository: CRAN Date/Publication: 2024-09-24 19:10:10 UTC Probabilistic PCA Description Function to perform (non-phylogenetic) probabilistic PCA. This function is a modification (fork) of Rdimtools::do.ppca . Usage ProbPCA(x, ret_dim = 2) Arguments x a matrix with traits in columns and observations in rows. ret_dim number of dimensions (PC axes) to be kept by the model. Details This function uses the same algorithm as Rdimtools::do.ppca. However, it returns more details from the estimation and computes AIC and AICc. The function returns a list with the following elements. scores: the scores of the principal components; projection: the eigenvectors; sig: the MLE of the error of the model; mle.W: the MLE of the W matrix: varnames: the names of the traits; loglik: the log-likelihood of the estimate. Function also returns AIC, AICc, and BIC for the model. Value returns a list of class "phylPPCA". See "Details" for more information. References Tipping, M. E., and C. M. Bishop. 1999. Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology 61(3):611–622. doi: 10.1111/1467-9868.00196 Examples dt <- as.matrix( ratematrix::anoles$data[,1:3] ) ppca <- ProbPCA(x = dt, ret_dim = 2) doBiplot(x = ppca, add_margin = 0.3) Make biplot for any type of PCA Description Function to make biplots for any kind of PCA. It accepts the outputs from standard PCA (princomp and prcomp). It also works with the "phylProbPCA" and "ProbPCA" functions. It provides more options to the plot than the standard "stats::biplot". Usage doBiplot(x, choices = 1L:2L, scale = 1, pc.biplot = FALSE, col, ...) Arguments x output from PCA analysis. choices numeric vector of length 2. Use to choose which of the PC axes to plot. Default plots first and second axes: "choices = c(1,2)". scale numeric value between 0 and 1. Same as in "stats::biplot.princomp". See ?biplot.princomp for more information. pc.biplot logical. If TRUE it will produce a "principal component biplot" (sensu Gabriel, 1971). Same as in "stats::biplot.princomp". See ?biplot.princomp for more information. col character vector of length 3 with the colors of the biplot. First color is used for the score points (or sample sames), second color for arrows and variable names, and third color for the right and top-side ticks (plot axes). ... extra parameters for the function. Same as "stats::biplot". Details Function has the same options as "stats::biplot", with the addition of the following arguments. "plot_dimnames" controls is the names of the samples (species) will be plotted. "add_points" controls if the score points will be plotted. "add_margin" is a numeric value that expands the area of the plot. You can use this to make sure the names of variables and samples (species) fit the plot. Value makes a biplot of the PCA results. Examples phy <- ratematrix::anoles$phy[[1]] dt <- as.matrix( ratematrix::anoles$data[,1:3] ) ## Using probabilistic phylogenetic PCA phylppca <- phylProbPCA(phy = phy, x = dt, ret_dim = 2) doBiplot(x = phylppca, add_margin = 0.3) ## Using standard phylogenetic PCA phylpca <- phytools::phyl.pca(tree = phy, Y = dt) doBiplot(x = phylpca, add_margin = 0.3) ## Using probabilistic PCA ppca <- ProbPCA(x = dt) doBiplot(x = ppca, add_margin = 0.3) ## Using standard PCA pca1 <- princomp(x = dt) doBiplot(x = pca1, add_margin = 0.1) ## Using standard PCA pca2 <- prcomp(x = dt) doBiplot(x = pca2, add_margin = 0.1) Probabilistic Phylogenetic PCA Description Function to perform probabilistic phylogenetic PCA. Allows for fit of alternative models of trait evolution using branch length transformation. Usage phylProbPCA(phy, x, ret_dim = 2, model = "BM", quiet = FALSE) Arguments phy phylogeny in "phylo" format. x a matrix with traits in columns and species values in rows. Rownames must match the tip labels of phylogeny. ret_dim number of dimensions (PC axes) to be kept by the model. model choice of model of trait evolution. One of "BM", "lambda", "OU", or "EB". quiet if function should suppress output to the console while running Details The function can be used to estimate the probabilistic phylogenetic PCA (3PCA) using distinct models of trait evolution. Models are implemented using branch length transformation. Model fitting happens in two steps. First the maximum likelihood of the evolutionary covariance matrix (R) and the parameter of the model is estimated. Then the 3PCA model is estimated using the phylogenetic tree with branch lengths transformed following the MLE for the parameter of each trait evolution model. The function returns a list with the following elements. scores: the scores of the principal components; e_values: eigenvalues; e_vectors: eigenvectors or the projection; model_fit: information about the trait evolution model; loadings: the loadings of the PCs; varnames: the names of the variables; sig: the MLE of the error; mle.W: the MLE of the W matrix; Function also returns AIC, AICc, and BIC for the model. Value returns a list of class "phylPPCA". See "Details" for more information. References Revell, L. J. 2009. Size-Correction and Principal Components for Interspecific Comparative Studies. Evolution 63:3258–3268. doi: 10.1111/j.1558-5646.2009.00804.x Revell, L. J. 2024. phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ 12:e16505. doi: 10.7717/peerj.16505 Tipping, M. E., and C. M. Bishop. 1999. Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology 61(3):611–622. doi: 10.1111/1467-9868.00196 Examples phy <- ratematrix::anoles$phy[[1]] dt <- as.matrix( ratematrix::anoles$data[,1:3] ) ppca <- phylProbPCA(phy = phy, x = dt, ret_dim = 2) doBiplot(x = ppca, add_margin = 0.3)