R/bic.mixture.univariate.R

In netresponse: Functional Network Analysis

#' @title Univariate BIC mixture
#' @description Latent class analysis based on (infinite) Gaussian mixture
#' model. If the input (dat) is data matrix, a multivariate model is fitted. If
#' the input is a vector or a 1-dimensional matrix, a univariate model is
#' fitted.
#' @param x  dat vector (for univariate analysis) or a matrix (for multivariate analysis)
#' @param max.modes Maximum number of modes to be checked for mixture model selection
#' @param bic.threshold BIC threshold which needs to be exceeded before a new mode is added to the mixture.
#' @param min.modes minimum number of modes
#' @param ... Further optional arguments to be passed
#' @return Fitted latent class model (parameters and free energy)
#' @author Contact: Leo Lahti \email{leo.lahti@@iki.fi}
#' @references See citation('netresponse')
#' @keywords utilities
bic.mixture.univariate <- function(x, max.modes, bic.threshold = 0, min.modes = 1, 
    ...) {

    
    # x <- datamatrix[, node]; max.modes = params$max.responses; bic.threshold =
    # params$bic.threshold
    
    best.mode <- bic.select.best.mode(x, max.modes, bic.threshold, min.modes = min.modes)
    mcl <- Mclust(x, G = best.mode)
    
    means <- as.vector(mcl$parameters$mean)
    sds <- as.vector(sqrt(mcl$parameters$variance$sigmasq))
    if (length(sds) == 1) {
        sds <- rep(sds, length(means))
    }
    ws <- as.vector(mcl$parameters$pro)
    
    if (is.null(ws)) {
        warning("NULL weights, replacing with 1")
        ws <- 1
    }
    if (is.null(means)) {
        warning("NULL means, replacing with 1")
        means <- 1
    }
    if (is.null(sds)) {
        warning("NULL sds, replacing with 1")
        sds <- 1
    }
    
    Nparams <- length(means) + length(sds) + length(ws)
    
    means <- matrix(means, nrow = length(ws))
    sds <- matrix(sds, nrow = length(ws))
    
    # Determine the most likely mode for each sample (-> hard clusters) save(means,
    # sds, ws, x, file = '~/tmp/tmp.RData')
    qofz <- P.r.s(matrix(x, nrow = 1), list(mu = means, sd = sds, w = ws), log = FALSE)
    rownames(qofz) <- names(x)
    
    names(means) <- names(sds) <- names(ws) <- paste("Mode", seq_len(length(ws)), 
        sep = "-")
    
    list(means = means, sds = sds, ws = ws, Nparams = Nparams, free.energy = -mcl$loglik, 
        qofz = qofz)
    
}