https://github.com/willgearty/pcmtools
Tools for Phylogenetic Comparative Methods
https://github.com/willgearty/pcmtools
comparative methods phylogenetic
Last synced: 6 months ago
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Tools for Phylogenetic Comparative Methods
- Host: GitHub
- URL: https://github.com/willgearty/pcmtools
- Owner: willgearty
- Created: 2019-10-07T17:48:09.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-29T22:52:45.000Z (over 6 years ago)
- Last Synced: 2025-10-05T07:49:32.822Z (6 months ago)
- Topics: comparative, methods, phylogenetic
- Language: R
- Homepage:
- Size: 35.2 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[](https://travis-ci.com/willgearty/pcmtools)
[](https://zenodo.org/badge/latestdoi/213449197)
# pcmtools
Various tools to help with performing phylogenetic comparative methods and curating/visualizing the results.
## To install
```r
library(devtools)
install_github("willgearty/pcmtools")
```
## To use
### Load package
```r
library(pcmtools)
```
### OUwie Post-Analysis Tools
```r
library(OUwie)
data(tworegime)
ou.results <- list()
ou.results[[1]] <- OUwie(tree,trait,model=c("BM1"))
ou.results[[2]] <- OUwie(tree,trait,model=c("BMS"), root.station = FALSE)
ou.results[[3]] <- OUwie(tree,trait,model=c("OUM"))
ou.results[[4]] <- OUwie(tree,trait,model=c("OUMV"))
#Both regimes have same parameters for BM1 model. Both regimes have different parameters for other models.
regime.mat <- data.frame(BM1 = c(1, 1), BMS = c(1,2), OUM = c(1,2), OUMV = c(1,2), row.names = c(1,2))
#Extract and map parameters to regimes
ou.parameters <- OUwieParSumm(ou.results, regime.mat)
#Summarize model fit
ou.aic <- OUwieAICSumm(ou.parameters)
#Model average parameters
ou.avg <- OUwieModelAvg(ou.parameters)
```
### Rename, merge, or split mapped states
```r
library(phytools)
#Simulate a mapped tree
set.seed(4)
Q <- matrix(c(-2,1,1,1,-2,1,1,1,-2),3,3)
rownames(Q) <- colnames(Q) <- letters[1:3]
tree <- sim.history(pbtree(n=100,scale=1),Q)
cols <- setNames(c("blue","red","green","orange"),letters[1:4])
#Plot the mapping
plot(tree, cols, ftype="i", fsize=0.7)
#Split state c to state d within subclade
tree2 <- mergeMappedStates2(tree, "c", "d", 173)
#Plot new mapping
plot(tree2, cols, ftype="i", fsize=0.7)
```
### Extract posterior values from densityMaps and contMaps
```r
library(phytools)
#Simulate tree
tree <- pbtree(n=70,scale=1)
#Simulate discrete trait
Q <- matrix(c(-1,1,1,-1),2,2)
rownames(Q) <- colnames(Q)<-c(0,1)
x1 <- sim.history(tree,Q)$states
#Generate stochastic maps and density map
mtrees <- make.simmap(tree,x1,nsim=100)
map1 <- densityMap(mtrees)
#Simulate continuous trait
x2 <- fastBM(tree,sig2=0.1)
#Generate cont map
map2 <- contMap(tree, x2)
#Extract posterior densities
pp_data <- extractSimmapDensity(map1, map2)
#Plot to see how traits have evolved with respect to one another
plot(pp_data$map1, pp_data$map2)
```
### Calculating phenotype statistics through time
```r
library(phytools)
#Simulate tree
tree <- pbtree(n=70,scale=1)
#Simulate discrete trait
Q <- matrix(c(-1,1,1,-1),2,2)
rownames(Q) <- colnames(Q)<-c(0,1)
tree <- sim.history(tree,Q)
#Simulate continuous trait
x2 <- fastBM(tree,sig2=0.1)
#Calculate stats through time
tstt <- traitStatsThroughTime(tree, x2)
coords <- tstt$xy
stats <- tstt$stats
#Plot phenogram
ys <- c(coords$y1, coords$y2)
plot(NULL, xlim=c(0,1), ylim=c(min(ys), max(ys)), ylab="trait", xlab="time")
segments(coords$x1, coords$y1, coords$x2, coords$y2)
#Plot stats
plot(stats$x, stats$mean)
```
### Coming Soon...
- Plotting phenotypes through time
- Plotting OU models
- And more!