Description Usage Arguments Details Value Examples
Efficient inversion of the covariance matrix estimated from PPCA.
1 | ppca2Covinv(ppcaOutput)
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ppcaOutput |
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The computation exploits the Woodbury identity so that a kxk matrix (where k is often less than 10) is inverted instead of the potentially large pxp matrix. The closed-form expression for the inverse depends upon parameters that are estimated in the PPCA algorithm.
matrix
– the inverse of the covariance matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # simulate a dataset from a zero mean factor model X = Wz + epsilon
# start off by generating a random binary connectivity matrix
n.factors <- 5
n.genes <- 200
# with dense connectivity
# set.seed(20)
conn.mat <- matrix(rbinom(n = n.genes*n.factors,
size = 1, prob = 0.7), c(n.genes, n.factors))
# now generate a loadings matrix from this connectivity
loading.gen <- function(x){
ifelse(x==0, 0, rnorm(1, 0, 1))
}
W <- apply(conn.mat, c(1, 2), loading.gen)
# generate factor matrix
n.samples <- 100
z <- replicate(n.samples, rnorm(n.factors, 0, 1))
# generate a noise matrix
sigma.sq <- 0.1
epsilon <- replicate(n.samples, rnorm(n.genes, 0, sqrt(sigma.sq)))
# by the ppca equations this gives us the data matrix
X <- W%*%z + epsilon
WWt <- tcrossprod(W)
Sigma <- WWt + diag(sigma.sq, n.genes)
# select 10% of entries to make missing values
missFrac <- 0.1
inds <- sample(x = 1:length(X),
size = ceiling(length(X)*missFrac),
replace = FALSE)
# replace them with NAs in the dataset
missing.dataset <- X
missing.dataset[inds] <- NA
# run ppca
ppf <- pca_full(missing.dataset, ncomp=5, algorithm="vb", maxiters=5,
bias=TRUE, rotate2pca=FALSE, loglike=TRUE, verbose=TRUE)
# compute the inverse
covinv <- ppca2Covinv(ppf)
system.time(ppca2Covinv(ppf))
covinv2 <- solve(ppf$Sigma)
system.time(solve(ppf$Sigma))
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