cv.cgpca: CV for convex generalized PCA

In andland/generalizedPCA: Generalized PCA

Description

Run cross validation on dimension and M for convex generalized PCA

Usage

cv.cgpca(x, ks, Ms = seq(2, 10, by = 2), folds = 5, quiet = TRUE, ...)

Arguments

x	matrix with all binary entries
ks	the different dimensions k to try
Ms	the different approximations to the saturated model M to try
folds	if folds is a scalar, then it is the number of folds. If it is a vector, it should be the same length as the number of rows in x
quiet	logical; whether the function should display progress
...	Additional arguments passed to convexGeneralizedPCA

Value

A matrix of the CV log likelihood with k in rows and M in columns

Examples

# construct a low rank matrix in the logit scale
rows = 100
cols = 10
set.seed(1)
mat_logit = outer(rnorm(rows), rnorm(cols))

# generate a binary matrix
mat = (matrix(runif(rows * cols), rows, cols) <= inv.logit.mat(mat_logit)) * 1.0

## Not run: 
loglikes = cv.cgpca(mat, ks = 1:9, Ms = 3:6)
plot(loglikes)

## End(Not run)

andland/generalizedPCA documentation built on May 12, 2019, 2:42 a.m.