R/F_liks.R

In CenterForStatistics-UGent/RCM: Fit row-column association models with the negative binomial distribution for the microbiome

#' Calculate the log-likelihoods of all possible models
#'
#'@param rcm an object of the RCM class
#'@param Sum a boolean, should log-likelihoods be summed?
#'
#'@return If Sum is FALSE, a named array log-likelihoods
#' of the independence model and all models with dimension 1 to k,
#' including after filtering on confounders.
#' Otherwise a table with log-likelihoods,
#' deviance explained and cumulative deviance explained.
#'@export
#' @examples
#' data(Zeller)
#' require(phyloseq)
#' tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
#' prune_samples(sample_names(Zeller)[1:50], Zeller))
#' zellerRCM = RCM(tmpPhy, round = TRUE)
#' liks(zellerRCM)
liks = function(rcm, Sum = TRUE) {
    vec = if (length(rcm$confModelMat))
        c(0, 0.5, seq_len(rcm$k), Inf) else c(0:rcm$k, Inf)
    outnames = c("independence", if (length(rcm$confModelMat))
    "filtered" else NULL,
        paste0("Dim", seq_len(rcm$k)), "saturated")
    if (Sum) {
        tmp = vapply(FUN.VALUE = numeric(1), vec, FUN = function(i) {
            sum(getLogLik(rcm, i), na.rm = TRUE)
        })
        names(tmp) = outnames
    } else {
        tmp = vapply(vec, FUN.VALUE = matrix(0, nrow(rcm$X), ncol(rcm$X)),
            FUN = function(i) {
                getLogLik(rcm, i)
            })
        dimnames(tmp)[[3]] = outnames
    }
    if (Sum) {
        # Also make cumulative comparisons
        cumDevianceExplained = round((tmp - tmp[1])/(tmp[length(tmp)] -
            tmp[1]), 3)
        out = rbind(logLikelihood = tmp, logLikExplained = c(0,
        diff(cumDevianceExplained)),
        cumLogLikExplained = cumDevianceExplained)
    }
    return(out)
}