ZIPPCApn: ZIPPCA

In YanyZeng/ZIPPCA: Zero-Inflated Probabilistic PCA (ZIPPCA, pronounced zipcar)

Description

Zero-Inflated Probabilistic PCA (ZIPPCA), for dimension reduction and data ordination of multivariate abundance data, and propose an efficient variational approximation method for estimation, inference, and prediction.

Usage

ZIPPCApn(X, V = NULL, family = "negative.binomial", n.factors = 2,
         rank = FALSE, trace = FALSE, maxit = 100, parallel=TRUE)

Arguments

X	matrix of observations.
V	vector of the sample covariate.
family	distribution of models. Two options are "poisson" and "negative.binomial". Defaults to "negative.binomial".
n.factors	the rank or number of factors, after dimensional reduction. Defaults to 2.
rank	logical, if TRUE, the rank or number of factors, is chosen from 1 to 5 by HIC (hybrid information criterion). Defaults to FALSE.
trace	logical, defaults to FALSE. if TRUE each current iteration step information will be printed.
maxit	maximum number of iterations within optim and constrOptim function, defaults to 100.
parallel	logical, if TRUE, use parallel toolbox to accelerate.

Value

VLB	variational lower bound of log likelihood
lvs	list of latent variables pi the probabilities of excess zeros factor_scores coordinates or factor scores in low-dimensional subspace
params	list of model parameters factor_coefs_j coefficients of latent variables fator scores or factor loadings factor_coefs_0 taxon-specific intercepts alpha sample-specifc intercepts that adjusts for the sequencing depth gamma coeffcients of sample covariate dispersion taxon-specific over-dispersion parameter for negative binomial distribution
hic	the number of the rank selection, chosen by HIC type information criterion

Examples

 n.n = 100
 n.m = 50
 n.factors = 2
 set.seed(37)
 f <- matrix(0,nrow = n.n, ncol = n.factors)
 for(i in 1:n.n){
   f[i,] <- rnorm(n.factors, mean = 0, sd = 1)
 }
 betaj <- matrix(0,nrow = n.m, ncol = n.factors)
 for(j in 1:n.m){
   betaj[j,] <- rnorm(n.factors, mean =0, sd = 1)
 }
 beta0 <- rep(1,n.m)
 alpha <- rep(0,n.n)
 l <- matrix(alpha,n.n,n.m) +matrix(beta0,n.n,n.m,byrow=TRUE) + f %*% t(betaj)
 lambda <- exp(l)
 eta_j <- runif(n.m, 0,1)
 z <- matrix(0,n.n,n.m)
 for(i in 1:n.n){
   z[i,] <- rbinom(n.m, size=1, prob=eta_j)
 }
 X <- matrix(0,n.n,n.m,byrow = TRUE)
 for(i in 1:n.n){
   for(j in 1:n.m){
     X[i,j] <- rnbinom(n=1,size=10,mu=lambda[i,j])
   }
 }
 X[z==1]=0
 zerorow <- which(rowSums(X)==0)
 if(length(zerorow) >0 ){
   X <- X[-zerorow,];f <- f[-zerorow,]
 }
 zerocol <- which(colSums(X)==0)
 if(length(zerocol) >0 ){
   X <- X[,-zerocol];betaj <- t(t(betaj)[,-zerocol])
 }
 result <- ZIPPCA::ZIPPCApn(X)
 f_coordinates <- result$lvs$factor_scores

YanyZeng/ZIPPCA documentation built on March 17, 2021, 4:57 p.m.