subtractMu: Subtract the row means from a matrix of data with missing...

In HGray384/pcaNet: Probabilistic principal components analysis - covariance estimation and network reconstruction

Description

internal function within pca_full to subtract the row means from a matrix of data using only the observed values. Offers little utility standalone.

Usage

subtractMu(Mu, X, M, p, n, update_bias, verbose = TRUE)

Arguments

Mu	numeric – the sample mean of the observed variables.
X	matrix – the data matrix with variables in rows and observations in columns.
M	matrix – logical matrix whose values indicate whether the corresponding entry in X is observed.
p	numeric – the number of variables.
n	numeric – the number of observations.
update_bias	logical – whether the mean should be subtracted. or not.
verbose	logical – whether extra output should be displayed.

Value

X matrix – centered data matrix.

Examples

p <- 20
n <- 7
set.seed(10045)
X <- matrix(rnorm(p*n), p, n)
miss.inds <- sample(1:(p*n), (p*n)/4)
X[miss.inds] <- NA
M <- !is.na(X)
Nobs_i <- rowSums(M)
Mu <- rowSums(X, na.rm = TRUE) / Nobs_i
update_bias <- TRUE
Xcent <- subtractMu(Mu=Mu, X=X, M=M, p=p, n=n, update_bias=update_bias, verbose=TRUE)
X-Xcent
Mu # all observed values in each column equal to Mu

HGray384/pcaNet documentation built on Nov. 14, 2020, 11:11 a.m.