stepfit {MVB} | R Documentation |
stepwise fit multivariate log-linear Bernoulli model using Newton-Raphson algorithm.
stepfit(x, y, maxOrder = 2, output = 0, direction = c("backward", "forward"), tune = c("AIC", "BIC", "GACV", "BGACV"), start = NULL)
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. |
The stepfit
utilize the class structure of the underlying C++
code and stepwisd fitted the model with Newton-Raphson algorithm.
An object of class mvbfit
, for which some methods are
available.
mvblps
, unifit
, stepfit
, mvb.simu
# 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)