binreg: Binary regression models.

In dnzmarcio/binreg: binreg R package

Description

Performs the Maximum Likelihood Estimation (MLE) of some models for binary data.

Usage

 binreg(formula, link, data, subset, start, tol, iterlim, na.action)

Arguments

formula	an object of class 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted.
link	the link function to be used. The options are: 'Aranda-Ordaz', 'Weibull', 'CWeibull', 'Stukel', 'Prentice'.
data	an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in 'data', the variables are taken from 'environment(formula)'.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
start	initial guess to model's parameters. Generally it is not necessary.
tol	tolerance error used in the estimation.
iterlim	maximum number of iteration to be performed.
na.action	a function which indicates what should happen when the data contain 'NA's. More details see 'glm' function.

Details

It is a function to facilitate de use of LaplacesDemon function. Transform a formula of binary regression model in data to be used with LaplacesDemon function, and suggest an command to run the LaplacesDemon.

Value

'binreg' returns an object of class inheriting from 'glm'. For more details see 'glm' function.

See Also

glm

Examples

  set.seed(666)
  x <- rnorm(0,1)
  y <- rbern(length(x),pnorm(0.5+2*x))
  fit <- binreg(y ~ x, "Aranda")
  summary(fit)

dnzmarcio/binreg documentation built on May 15, 2019, 9:41 a.m.