mvblps: multivariate Bernoulli LASSO model fitting

In MVB: Mutivariate Bernoulli log-linear model

Description

fit multivariate Bernoulli LASSO model accelerated block-coordinate relaxation algorithm.

Usage

mvblps(x, y, maxOrder = 2, lambda = NULL, nlambda = 100,
       lambda.min.ratio = ifelse(nobs<nvars, .01, .0001),
       output = 0, printIter = 100, search = c('nm', 'grid'),
       tune = c("AIC", "BIC", "GACV", "BGACV"))

Arguments

x	input design matrix.
y	output binary matrix with number of columns equal to the number of outcomes per observation.
maxOrder	maximum order of interactions to be considered in outcomes.
lambda	a user specified tuning sequece. Typical usage is to have the program compute its own lambda.
nlambda	the number of lambda values, default is 100.
lambda.min.ratio	Smallest value for lambda, as a fraction of lambda.max. The default depends on the sample size nobs relative to the number of variables.
output	with values 0 or 1, indicating whether the fitting process is muted or not.
printIter	Number of iterations to be printed if output is true.
search	Tuning search approach, nm for Nelder Mead and grid for grid search.
tune	tuning approach, available methods including AIC, BIC, GACV, BGACV.

Details

The mvblps utilize the class structure of the underlying C++ code and fitted the model with accelerated block-coordinate relaxation algorithm.

Value

An object of classes mvbfit and lps, for which some methods are available.

See Also

mvbfit, unifit, stepfit, mvb.simu

Examples

# fit a simple MVB log-linear model
n <- 1000
p <- 5
kk <- 2
tt <- NULL
alter <- 1
for (i in 1:kk) {
  vec <- rep(0, p)
  vec[i] <- alter
  alter <- alter * (-1)
  tt <- cbind(tt, vec)
}
tt <- 1.5 * tt
tt <- cbind(tt, c(rep(0, p - 1), 1))

x <- matrix(rnorm(n * p, 0, 4), n, p)
res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
fitMVB <- mvblps(x, res$response, output = 1)

MVB documentation built on May 2, 2019, 3:06 a.m.