cv.clpca: CV for convex logistic PCA

In logisticPCA: Binary Dimensionality Reduction

Description

Run cross validation on dimension and m for convex logistic PCA

Usage

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

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
Ms	depricated. Use ms instead
...	Additional arguments passed to convexLogisticPCA

Value

A matrix of the CV negative 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: 
negloglikes = cv.clpca(mat, ks = 1:9, ms = 3:6)
plot(negloglikes)

## End(Not run)

logisticPCA documentation built on May 1, 2019, 10:12 p.m.