ZIPPCAlnm: ZIPPCAlnm

In YanyZeng/ZIPPCAlnm: Zero-Inflated Probabilistic PCA with logistical normal multinomial (ZIPPCA-LNM)

Description

Zero-Inflated Probabilistic PCA framework with logistical normal multinomial distribution (ZIPPCA-LNM), that extends probabilistic PCA from the Gaussian setting to multivariate abundance data, and an empirical Bayes approach was proposed for inferring microbial compositions. An efficient VA algorithm, classification VA, has been developed for fitting this model.

Usage

ZIPPCAlnm(
  X,
  V = NULL,
  n.factors = 2,
  rank = FALSE,
  trace = FALSE,
  maxit = 100,
  parallel = TRUE,
  sd.errors = FALSE,
  level = 0.95
)

Arguments

X	count matrix of observations.
V	vector of the sample covariate.
n.factors	the rank or number of factors, after dimensional reduction. Defaults to 2.
rank	FALSE, "BIC" or "CV". Indicating whether the rank or number of factors, is chosen from 1 to 5. Options are "BIC" (Bayesian information criterion), and "CV" (Cross-validation). BIC is recommended. 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.
sd.errors	logical, defaults to FALSE. if TRUE the sandwich estimators will be calculated.
level	the confidence level of variational confidence interval. Defaults to 0.95.

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 gamma coeffcients of sample covariate
Q	the underlying composition of microbiome data
bic	the number of the rank selection, chosen by BIC type information criterion
cv	the number of the rank selection, chosen by cross-validation
sd	standard deviation of the sandwich estimators
conf	variational confidence intervals of model parameters

Examples

n.n = 50
n.w = 100
n.factors = 2
set.seed(1)
si <- diag(n.factors)
me <- c(0,0)
f <- matrix(0,nrow = n.n, ncol = n.factors)
for(i in 1:n.n){
 f[i,] <- mvrnorm(1,me,si)
}
betaj <- matrix(0,nrow = n.w, ncol = n.factors)
for(j in 1:n.w){
  betaj[j,] <- runif(n.factors,-3.5,3.5)
}
beta0 <- rep(2,n.w)
l <- matrix(beta0,n.n,n.w,byrow=TRUE)+f %*%  t(betaj)
eta_j <- rep(0.25,n.w)
z <- matrix(0,n.n,n.w,byrow = TRUE)
for(i in 1:n.n){
  z[i,] <- rbinom(n.w, size=1, prob=eta_j)
}
sum <- rowSums(exp(l))
Qn <- exp(l)/sum
sum <- matrix(rowSums((1-z)*exp(l)),n.n,n.w)
Qn_z <- matrix(I(z==0)*(exp(l)/sum)+I(z==1)*0,n.n,n.w)
X <- matrix(0,n.n,n.w,byrow = TRUE)
for(i in 1:n.n){
  X[i,] <- rmultinom(1, size = runif(1,800,1000), prob = Qn_z[i,])
}
zerorow <- which(rowSums(X)==0)
if(length(zerorow) >0 ){
  X <- X[-zerorow,];Qn <- Qn[-zerorow,];Qn_z <- Qn_z[-zerorow,];
}
zerocol <- which(colSums(X)==0)
if(length(zerocol) >0 ){
  X <- X[,-zerocol];Qn <- Qn[,-zerocol];Qn_z <- Qn_z[,-zerocol];
}
result <- ZIPPCAlnm::ZIPPCAlnm(X)

YanyZeng/ZIPPCAlnm documentation built on May 9, 2021, 3:55 p.m.