unilps: univariate model fitting with lasso penalty
In MVB: Mutivariate Bernoulli log-linear model

Description Usage Arguments Details Value See Also Examples

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 `unifit` is called.
`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 `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.
`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')