R/getPredictErr.gllvm.R

In gllvm: Generalized Linear Latent Variable Models

#' @title Extract prediction errors for latent variables from gllvm object
#' @description  Calculates the prediction errors for latent variables and random effects for gllvm model.
#'
#' @param object   an object of class 'gllvm'.
#' @param CMSEP logical, if \code{TRUE} conditional mean squared errors for predictions are calculated. If \code{FALSE}, prediction errors are based on covariances of the variational distributions for \code{method ="VA"} and \code{method ="EVA"}.
#' @param cov if \code{TRUE}, return as covariances/variances of predictions. Otherwise \code{FALSE} (default) return as standard errors of predictions.
#' @param ...	 not used
#'
#' @details 
#' Calculates conditional mean squared errors for predictions.
#' If variational approximation is used, prediction errors can be based on covariances 
#' of the variational distributions, and therefore they do not take into account 
#' the uncertainty in the estimation of (fixed) parameters. 
#'
#' @return Function returns following components:
#'  \item{lvs }{prediction errors for latent variables}
#'  \item{row.effects }{prediction errors for random row effects if included}
#'
#' @author Francis K.C. Hui, Jenni Niku, David I. Warton
#'
#' @examples
#'\dontrun{
#'# Load a dataset from the mvabund package
#'data(antTraits)
#'y <- as.matrix(antTraits$abund)
#'# Fit gllvm model
#'fit <- gllvm(y = y, family = poisson())
#'# prediction errors for latent variables:
#'getPredictErr(fit)
#'}
#'
#'@aliases getPredictErr getPredictErr.gllvm
#'@method getPredictErr gllvm
#'@export
#'@export getPredictErr.gllvm
getPredictErr.gllvm = function(object, CMSEP = TRUE, cov = FALSE, ...)
{
  if(!is.list(object$sd)){
    stop("Cannot calculate prediction errors without standard errors in the model.")
  }
  
  n <- nrow(object$y)
  num.lv <- object$num.lv
  num.lv.c <- object$num.lv.c
  num.RR <- object$num.RR
  
  if((num.lv.c+num.lv)==0&object$randomB==FALSE&object$row.eff!="random"&is.null(object$randomX)){
    stop("Cannot calculate prediction errors without random-effects in the model.")
  }
    
  out <- list()
  
  if(object$method == "LA"){
    if(cov){
      if((num.lv+num.RR+num.lv.c)>0) out$lvs <- object$prediction.errors$lvs
      if(object$row.eff == "random") out$row.effects <- object$prediction.errors$row.params
      if(object$randomB!=FALSE) out$b.lv <- object$prediction.errors$Ab.lv
      if(!is.null(object$randomX)){
        out$Br  <- object$prediction.errors$Br
      }
    } else {
      if((num.lv+num.RR+num.lv.c)>0) out$lvs <- sqrt(apply(object$prediction.errors$lvs,1,diag))
      if(object$row.eff == "random") out$row.effects <- sqrt(abs(object$prediction.errors$row.params))
      if(object$randomB!=FALSE) out$b.lv <- sqrt(abs(object$prediction.errors$Ab.lv))
      if(!is.null(object$randomX)){
        out$Br  <- sqrt(apply(object$prediction.errors$Br,1,diag))
      }
    }
  }
  

  if((object$method %in% c("VA", "EVA"))){
    if(object$row.eff == "random"){
      if(is.null(object$Ar)){
        if(dim(object$A)[3]>ncol(object$lvs)){
          object$Ar<-object$A[,1,1]
          object$A<-object$A[,-1,-1]
        }
      }
    }
    if(CMSEP) {
      sdb <- CMSEPf(object)
      # sdb<-sdA(object)
      if(num.RR>0){
        #variational covariances but add 0s for RRR
        A <- array(0,dim=c(n,num.lv.c+num.RR+num.lv,num.lv.c+num.RR+num.lv))
        A[,-c((num.lv.c+1):(num.lv.c+num.RR)),-c((num.lv.c+1):(num.lv.c+num.RR))] <- object$A
      } else if((object$num.lvcor > 1) && (object$Lambda.struc %in% c("diagU","UNN","UU"))) {
          A<-array(diag(object$A[,,1]), dim = c(nrow(object$A[,,1]), object$num.lvcor,object$num.lvcor))
          for (i in 1:dim(A)[1]) {
            A[i,,]<-A[i,,]*object$AQ
          }
      } else if((object$num.lvcor > 0) & (object$corP$cstruc !="diag")) {
        A<-array(0, dim = c(nrow(object$A[,,1]), object$num.lvcor,object$num.lvcor))
        for (i in 1:object$num.lvcor) {
          A[,i,i]<- diag(object$A[,,i])
        }
        if(object$num.lvcor==1) A <- A[,1,1]
      }else if((num.lv.c+num.lv)>0){
        A<-object$A
        if((num.lv.c+num.lv)==1) A <- A[,1,1]
      }
      
      if(object$row.eff == "random"){
        object$Ar<-sdb$Ar[,1]+object$Ar
      }
      
      if(!is.null(object$randomX)){
        object$Ab <- sdb$Ab+object$Ab
      }
      
      if((num.lv+num.lv.c)>0){ object$A<-sdb$A+A} else{object$A <- sdb$A}
      if(num.RR>0&object$randomB!=FALSE){
        if(object$randomB=="P"|object$randomB=="single"){
          covsB <- as.matrix(Matrix::bdiag(lapply(seq(dim(object$Ab.lv)[1]), function(k) object$Ab.lv[k , ,])))
        }else if(object$randomB=="LV"){
          covsB <- as.matrix(Matrix::bdiag(lapply(seq(dim(object$Ab.lv)[1]), function(q) object$Ab.lv[q , ,])))
        }
        
        for(i in 1:n){
          Q <- as.matrix(Matrix::bdiag(replicate(num.RR+num.lv.c,object$lv.X[i,,drop=F],simplify=F)))
          temp <- Q%*%covsB%*%t(Q) #variances and single dose of covariances
          temp[col(temp)!=row(temp)] <- 2*temp[col(temp)!=row(temp)] ##should be double the covariance
          A[i,1:(num.RR+num.lv.c),1:(num.RR+num.lv.c)] <- A[i,1:(num.RR+num.lv.c),1:(num.RR+num.lv.c)] + temp
        }
        object$A <- A
        
      }
    }else if(!CMSEP&(num.RR+num.lv.c)>0){
      sdb <- list(Ab_lv = 0)
      
      if(num.RR>0&object$randomB!=FALSE){
        if(object$randomB=="P"|object$randomB=="single"){
          covsB <- as.matrix(Matrix::bdiag(lapply(seq(dim(object$Ab.lv)[1]), function(k) object$Ab.lv[k , ,])))
        }else if(object$randomB=="LV"){
          covsB <- as.matrix(Matrix::bdiag(lapply(seq(dim(object$Ab.lv)[1]), function(q) object$Ab.lv[q , ,])))
        }
        
        for(i in 1:n){
          Q <- as.matrix(Matrix::bdiag(replicate(num.RR+num.lv.c,object$lv.X[i,,drop=F],simplify=F)))
          temp <- Q%*%covsB%*%t(Q) #variances and single dose of covariances
          # temp[col(temp)!=row(temp)] <- 2*temp[col(temp)!=row(temp)] ##should be double the covariance
          A[i,1:(num.RR+num.lv.c),1:(num.RR+num.lv.c)] <- A[i,1:(num.RR+num.lv.c),1:(num.RR+num.lv.c)] + temp
        }
        object$A <- A
        
      }
    }
  
  
  r=0
  if(cov){
    if(object$row.eff=="random"){
      # r=1
      out$row.effects <- (object$Ar)
    }

    if(length(dim(object$A))==2){
      out$lvs <- (object$A[,1:(num.lv+num.lv.c+num.RR)+r])
    } else  if((num.lv+num.lv.c+num.RR)>0){
      if((num.lv+num.lv.c+num.RR) ==1) {
        out$lvs <- (as.matrix(object$A[,1:(num.lv+num.lv.c+num.RR)+r,1:(num.lv+num.lv.c+num.RR)+r]))
      } else {
        out$lvs <- (object$A[,1:(num.lv+num.lv.c+num.RR)+r,1:(num.lv+num.lv.c+num.RR)+r])
      }
    }
    
    if(!is.null(object$randomX)){
      out$Br <- object$Ab
    }
    
    if(object$randomB!=FALSE){
      out$b.lv <- sdb$Ab_lv
      if(object$randomB=="P") out$b.lv <- (abs(out$b.lv + t(sapply(1:ncol(object$lv.X), function(k)diag(object$Ab.lv[k,,])))))
      if(object$randomB=="LV") out$b.lv <- (abs(out$b.lv + sapply(1:(object$num.RR+object$num.lv.c), function(k)diag(object$Ab.lv[k,,]))))
    }

  } else {
    if(object$row.eff=="random"){
      # r=1
      out$row.effects <- sqrt(object$Ar)
    }

    if(length(dim(object$A))==2&(num.lv+num.lv.c+num.RR)>0){
      out$lvs <- sqrt(object$A[,1:(num.lv+num.lv.c+num.RR)+r])
    } else if((num.lv+num.lv.c+num.RR)>0){
      if((num.lv+num.lv.c+num.RR) ==1) {
        out$lvs <- sqrt(abs(as.matrix(object$A[,1:(num.lv+num.lv.c+num.RR)+r,1:(num.lv+num.lv.c+num.RR)+r])))
      } else {
        out$lvs <- sqrt(abs(apply((object$A[,1:(num.lv+num.lv.c+num.RR)+r,1:(num.lv+num.lv.c+num.RR)+r]),1,diag)))
      }
    }
    
    if(!is.null(object$randomX)){
      out$Br <- sqrt(apply(object$Ab,1,diag))
    }
    
    if(object$randomB!=FALSE){
      out$b.lv <- sdb$Ab_lv
      if(object$randomB=="P")out$b.lv <- sqrt(abs(out$b.lv + t(sapply(1:ncol(object$lv.X), function(k)diag(object$Ab.lv[k,,])))))
      if(object$randomB=="LV")out$b.lv <- sqrt(abs(out$b.lv + sapply(1:(object$num.RR+object$num.lv.c), function(k)diag(object$Ab.lv[k,,]))))
    }
    
  }
  }

  if((num.lv+num.lv.c+num.RR) > 1 & is.matrix(out$lvs)) out$lvs <- t(out$lvs)
  
  return(out)
}

#'@export getPredictErr
getPredictErr <- function(object, ...)
{
  UseMethod(generic = "getPredictErr")
}