mvblps {MVB} | R Documentation |
fit multivariate Bernoulli LASSO model accelerated block-coordinate relaxation algorithm.
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"))
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 |
nlambda |
the number of |
lambda.min.ratio |
Smallest value for |
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, |
tune |
tuning approach, available methods including AIC, BIC, GACV, BGACV. |
The mvblps
utilize the class structure of the underlying C++
code and fitted the model with accelerated block-coordinate relaxation algorithm.
An object of classes mvbfit
and lps
, for which some methods are
available.
mvbfit
, 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 <- mvblps(x, res$response, output = 1)