hetprobit2: Heteroscedastic Probit Regression Models v2

In hetprobit2: Heteroscedastic Probit Regression Models v2

Description

Fitting heteroscedastic probit models via maximum likelihood.

Usage

hetprobit2(formula, data, subset, na.action, model = TRUE, y = TRUE, x = FALSE, control = hetprobit2_control(...), ...)


hetprobit2_fit(x, y, z = NULL, control, ...)

hetprobit2_control(maxit = 5000, start = NULL, ...)

Arguments

formula	FIXME
data	FIXME
subset	FIXME
na.action	FIXME
model	FIXME
x, y	FIXME
z	FIXME
...	FIXME
control, maxit, start	FIXME

Details

FIXME

Value

An object of class "hetprobit2".

References

FIXME

See Also

FIXME

Examples

## packages
require("glmx")

## data-generating process
dgp <- function(n = 100, coef = c(1, 1, -1, 0, 1, 0)) {
  d <- data.frame(
    x1 = runif(n, -1, 1),
    x2 = runif(n, -1, 1)
  )
  d$ystar <- rnorm(100,
    mean = coef[1] + coef[2] * d$x1 + coef[3] * d$x2,
    sd = exp(coef[4] + coef[5] * d$x1 + coef[6] * d$x2)
  )
  d$y <- ifelse(d$ystar > 0, 1, 0)
  return(d)
}

## data
set.seed(2017-05-20)
d <- dgp()

## ## model fitting (m0 with hetglm.fit from glmx package, m1 with hetprobit2 function)
m0 <- hetglm(y ~ x1 + x2, data = d)
m1 <- hetprobit2(y ~ x1 + x2, data = d)

## comparison of coefficients
cbind(coef(m0), coef(m1))

## comparison of log-Likelihoods
cbind(logLik(m0), logLik(m1))

hetprobit2 documentation built on May 2, 2019, 4:59 p.m.