unifit: univariate model fitting
In MVB: Mutivariate Bernoulli log-linear model

Description Usage Arguments Details Value See Also Examples

fit univariate log-linear model using Newton-Raphson algorithm.

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unifit(formula, data = list(),
       family = c("gaussian", "binomial"),
       output = 0)

`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 support gaussian and binomial.
`output`	with values 0 or 1, indicating whether the fitting process is muted or not.

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

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

unilps, mvbfit

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 <- unifit(res ~ x - 1, family = 'binomial')