skewProbit.fit {SPreg}

Fitting Binary Regression with a Skew-Probit Link Function

Description

It is the default fitting method for skewProbit.

Usage

skewProbit.fit(y, x, penalty = "Jeffrey", initial = NULL, 
	cvtCov = TRUE, delta0 = 3, level = 0.95)

Arguments

y

a design matrix of dimension n*p

x

a vector of response of length n

penalty

type of penalty function. Default option is "Jeffrey". "Cauchy" will give estimates with Cauchy prior penaly function. "Naive" will give ML estimates.

initial

a logical value. If specified, it will be used for the initial value of numerical optimization.

cvtCov

a logical value. If it is true, then all numerical values will be standardized to have mean zero and unit standard deviation.

delta0

an initial guess of skewness parameter.

level

a confidence level. Default value is 0.95.

Value

A list cotaining the following components:

coefficients

A named vector of coefficients

stderr

Standard errors of coefficients

zscore

Z-scores of coefficients

pval

p-values of coefficients

lower

Lower limits of confidence intervals

upper

Upper limits of confidence intervals

Author(s)

DongHyuk Lee, Samiran Sinha

References

Identifiability and bias reduction in the skew-probit model for a binary response. To appear in Journal of Statistical Computation and Simulation.

Examples

library(sn)
library(ucminf)

n <- 500
b0 <- 0.34
delta <- 4
b1 <- 1
b2 <- -0.7

set.seed(1234)
x1 <- runif(n, -2, 2)
x2 <- rnorm(n, sd = sqrt(4/3))
eta <- as.numeric(b0 + b1*x1 + b2*x2)
p <- psn(eta, alpha = delta)
y <- rbinom(n, 1, p)

x <- cbind(1, x1, x2)
## Not run: 
mod1 <- skewProbit.fit(y, x, penalty = "Jeffrey", cvtCov = FALSE)
mod2 <- skewProbit.fit(y, x, penalty = "Naive", cvtCov = FALSE)
mod3 <- skewProbit.fit(y, x, penalty = "Cauchy", cvtCov = FALSE)
mod1$coef
mod2$coef
mod3$coef


## End(Not run)