unilps {MVB} | R Documentation |
fit univariate log-linear model using accelerated block-coordinate relaxation algorithm.
unilps(formula, data = list(), family = c("gaussian", "binomial"), lambda = NULL, nlambda = 100, lambda.min.ratio = ifelse(nobs<nvars, .01, .0001), output = 0, tune = c("AIC", "BIC", "GACV", "BGACV"))
formula |
a symbolic description of the model to be fit. |
data |
an optional data frame containing the variables in the
model. By default the variables are taken from the
environment from which |
family |
a GLM family, currently suport gaussian and binomial. |
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. |
tune |
tuning approach, available methods including AIC, BIC, GACV, BGACV. |
The unilps
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.
unilps
, mvblps
n <- 100 p <- 4 x <- matrix(rnorm(n * p, 0, 4), n, p) eta <- x pr <- exp(eta) / (1+ exp(eta)) res <- rbinom(n, 1, pr) fit <- unilps(res ~ x - 1, family = 'binomial')