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
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