| Type: | Package |
| Title: | Bias Reduction in the Skew-Probit Model for a Binary Response |
| Version: | 1.0 |
| Date: | 2019-03-12 |
| Author: | DongHyuk Lee, Samiran Sinha |
| Maintainer: | DongHyuk Lee <leedhyuk@gmail.com> |
| Depends: | R (≥ 3.0) |
| Imports: | sn, ucminf |
| Description: | Provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>. |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Packaged: | 2019-03-16 01:24:34 UTC; cutri |
| Repository: | CRAN |
| Date/Publication: | 2019-03-16 07:54:05 UTC |
Fitting Binary Regression with a Skew-Probit Link Function
Description
This function fits a binary regression with a skew-probit link function. Naive MLE, Jeffrey's prior and Cauchy prior type of penalization are implemented to find estimates.
Usage
skewProbit(formula, data = list(), penalty = "Jeffrey", initial = NULL,
cvtCov = TRUE, delta0 = 3, level = 0.95)
Arguments
formula |
an object of class "formula" as in |
data |
an optional data frame, list or environment containing the variables in the model as in |
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. |
Details
This function uses ucminf package for optimization. Also package sn is necessary.
A detailed disscussion can be found in the reference below.
Value
An object of class skewProbit is returned with
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)
## Not run:
dat <- data.frame(y, x1 = x1, x2 = x2)
mod1 <- skewProbit(y ~ x1 + x2, data = dat, penalty = "Jeffrey", cvtCov = FALSE, level = 0.95)
mod2 <- skewProbit(y ~ x1 + x2, data = dat, penalty = "Naive", cvtCov = FALSE, level = 0.95)
mod3 <- skewProbit(y ~ x1 + x2, data = dat, penalty = "Cauchy", cvtCov = FALSE, level = 0.95)
summary(mod1)
summary(mod2)
summary(mod3)
## End(Not run)
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 |
x |
a vector of response of length |
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)