stepfit: step-wisd multivariate model fitting

In MVB: Mutivariate Bernoulli log-linear model

Description

stepwise fit multivariate log-linear Bernoulli model using Newton-Raphson algorithm.

Usage

stepfit(x, y, maxOrder = 2,
        output = 0,
        direction = c("backward", "forward"),
        tune = c("AIC", "BIC", "GACV", "BGACV"),
        start = NULL)

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.
output	with values 0 or 1, indicating whether the fitting process is muted or not.
direction	the mode of stepwise search and default is backward.
tune	tuning approach, available methods including AIC, BIC, GACV, BGACV.
start	starting object of type mvbfit.

Details

The stepfit utilize the class structure of the underlying C++ code and stepwisd fitted the model with Newton-Raphson algorithm.

Value

An object of class mvbfit, for which some methods are available.

See Also

mvblps, 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 <- mvbfit(x, res$response, output = 1)

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