1d:T789,# Fitting Binary Regression with a Skew-Probit Link Function It is the default fitting method for `skewProbit`. ```r 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. ## Returns 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 ## References Identifiability and bias reduction in the skew-probit model for a binary response. **To appear in Journal of Statistical Computation and Simulation**. ## Author(s) DongHyuk Lee, Samiran Sinha ## Examples ```r 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) ```

skewProbit.fit function