phylocurve features

Ancestral curve reconstruction

Function: phylocurve(formula, tree, data, ymin = 0.01, ymax = 0.99, ylength = 30, tip_coefficients, species_identifier = "species", verbose = FALSE)

Description: This function uses a PGLS-based method described in Goolsby (2015) to perform ancestral curve reconstruction. This function uses a fast tree transversal method via the phylolm package (Ho and Ane, 2014). Currently only logistic regression (glm with logit link) is implemented.

Estimation of evolutionary rates

Function: rate.mult(tree = tree, Y = Y, type = c("mult", "diag", "all"), method = c("REML", "ML"), error = c("none", "estimate", "supply"), error_n = 20, error_supply, model = "BM", fixed_sigma2, fixed_model_pars)

Description: This function estimates multivariate evolutionary rates (Adams 2014) using maximimum likelihood. The function must be run prior to running other multivariate functions, including K.mult, pgls.mult, compare.rate.mult, and compare. multivar.rate.mult.

Compare multivariate evolutionary rates

Function: compare.rate.mult(rate.mult.fitted, groups, fit_individual = FALSE)

Description: This function compares multivariate evolutionary rates on a tree (Adams 2014) using maximimum likelihood (O'Meara et al. 2006).

Compare evolutionary rates of multiple multivariate traits

Function: Compare multiple multivariate evolutionary rates

Description: Compares hypotheses about evolutionary rates for multivariate traits combining methods from O'Meara et al. (2006), Adams (2013) and Adams (2014).

Phylogenetic signal

Function: K.mult(rate.mult.fitted, iter = 1000)

This function estimates multivariate phylogenetic signal (Adams 2014) on a fited rate.mult object using a covariance-based approach.

Phylogenetic regression

Function: pgls.mult(rate.mult.fitted, X)

Description: This function performs multivariate phylogenetic regression (Adams 2014) to assess the significance of correlated evolution between a multivariate trait and a univariate trait, using a covariance-based approach.