R/read.mulTree_fun.R

In TGuillerme/mulTree: Performs MCMCglmm on Multiple Phylogenetic Trees

## Reads a single model
get.mulTree.model <- function(mcmc.file) {
    model <- get(load(mcmc.file))
    ## Testing if the mcmc.file is the right object class
    if(class(model) != "MCMCglmm") {
        stop(paste("No MCMCglmm model found in file", mcmc.file))
    }
    return(model)
}

## Get the convergence diagnosis file
get.convergence <- function(conv.file){
    conv.name <- load(conv.file)
    converge <- get(conv.name)
    ## Testing if the converge object is the right object class
    check.class(converge, "gelman.diag")
    return(converge)
}

## Extracting some specific elements from the chain
get.element <- function(element, chain) {
    ## Getting the right files
    mcmc_files <- list.files(pattern = chain)
    mcmc_files <- mcmc_files[grep("_chain", mcmc_files)]

    ## Extracting all the models
    all_models <- lapply(as.list(mcmc_files), get.mulTree.model)
    ## Extracting the element
    all_elements <- sapply(all_models, "[[", element, simplify = FALSE)

    ## applying the names of to the list
    names(all_elements) <- paste(mcmc_files, element, sep="$")

    return(all_elements)
}

## Isolate the fixed terms (model$Sol) and the random terms (model$VCV) from a single model
get.table.mulTree <- function(mcmc.file) {
    ## Getting the fixed terms
    table_mcmc <- as.data.frame(mcmc.file$Sol)
    ## Adding the random terms
    random_terms <- c("phylogenetic.variance","residual.variance")
    table_mcmc[random_terms[1]] <- as.vector(mcmc.file$VCV[,1])
    table_mcmc[random_terms[2]] <- as.vector(mcmc.file$VCV[,2])
    ## Output
    return(as.list(table_mcmc))
}