Dear All, I am using a data set of about 600 plant species to test for phylogenetic signal in shifts in flowering time. The trait data are slopes generated by regressing year against flowering onset for each species. When I use phylosignal (picante) vs. phylosig (phytools) to get K, I get the same K values but different p-values, regardless of how many simulations I run. For example, with 20,000 simulations using phylosignal, K = 0.185 and p = 0.015; using phylosig, K = 0.185 and p = 0.073. My understanding is that the two methods use different approaches to generate p-values, but should arrive at the same answer. Any explanations for this difference, or ways to correct mistakes I am making, would be greatly appreciated.
Here is the code I�m using: library(picante) library(phytools) samp <- readsample("sampleslopes.txt") phy <- read.tree("davies_tree2013.txt") traits <- read.table("traitsslopes.txt", header = TRUE, row.names = 1) prunedphy <- prune.sample(samp, phy) samp <- samp[, prunedphy$tip.label] traits <- traits[prunedphy$tip.label, ] names(traits) <- prunedphy$tip.label is.binary.tree(prunedphy) prunedphy <- multi2di(prunedphy) is.binary.tree(prunedphy) phylosignal(traits, prunedphy, reps = 20000) K PIC.variance.obs PIC.variance.rnd.mean PIC.variance.P 1 0.185098 0.001584913 0.001817674 0.01519924 PIC.variance.Z 1 -1.901372 phylosig(prunedphy, traits, method = "K", test = TRUE, nsim = 20000) $K [1] 0.185098 $P [1] 0.07275 Many thanks in advance for your time and help. Nicole [[alternative HTML version deleted]]
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