cv.gmf: CV for generalized MF

In andland/generalizedPCA: Generalized PCA

Description

Run cross validation on dimension for generalized MF

Usage

cv.gmf(x, ks, family = c("gaussian", "binomial", "poisson", "multinomial"),
  folds = 5, quiet = TRUE, ...)

Arguments

x	matrix of either binary, count, or continuous data
ks	the different dimensions k to try
family	exponential family distribution of data
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 generalizedMF

Value

A matrix of the CV deviance with k in rows

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: 
deviances = cv.gmf(mat, ks = 1:9, family = "binomial")
plot(deviances)

## End(Not run)

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