README.md

R-package-Bayesian-Binary-Probit

###This R package implements the Bayesian Auxiliary variable Model for Binary Regression

##Reference : Holems, C., Held, L. Bayesian Auxiliary Variable Models for

##Binary and Multinomial Regression

##You can install the package from github by using the devtools package.

##If you want the tar.gz file, please write a mail to the author of the package aditi.jec31@gmail.com.

##Usage Example ##

library(devtools)

install_github("adu3110/R-package-bayesian-binary-probit")

library(BayesianBinaryProbit)

set.seed(250)

Create 1000 samples of two independent variables

N <- 1000

X1 <- gaussiansamplesbyCLT(N)

X2 <- gaussiansamplesbyCLT(N)

Create the matrix of input variables

X <- matrix(c(rep(1, N), X1, X2), ncol = 3)

True values of regression coeffiecients

true_coefficients <- c(-0.8, 1.5, 0.6)

Obtain the vector with probabilities of success p using the probit link

p <- pnorm(X %*% true_coefficients)

Generate binary observation data y

y <- rbinom(N, 1, p)

Fit the MLE Generalized Linear Model with probit link

fit <- glm(y ~ X1 + X2, family = binomial(link = probit))

fit$coefficients

Create input data frame for Bayesian Model

input_frame <- data.frame(resp = y, ind_var1 = X1, ind_var2 = X2)

system.time(

  bayesian_fit <- bayesianbinaryprobit(resp ~ ind_var1 + ind_var2, 
  
                                       data = input_frame, covar_prior = diag(10, 3), 
                                       
                                       num_mcmc = 20000, burn_in = 5000, thinning = 10)
                                       
)

bayesian_fit



##################Other Functions in the package##

##Gaussian Samples ##

hist(gaussiansamplesbyCLT(num_samples = 400, num_uniform_samples = 100, 

                     mean_norm = 5, sd_norm = 3))

#One sided truncated gaussian distribution

hist(samplegaussianonesided(800, side = "left", num_uniform_samples = 50))

hist(samplegaussianonesided(800, cut_off = 0.5, mean_norm = 2, sd_norm = 1,

                            side = "right", num_uniform_samples = 50))

Inverse of a matrix by gauss elimination

gausseliminationinverse(matrix(c(2,1,1,0), 2, 2))

Cholesky Lower Decomposition

choleskylower(matrix(c(2,-1,0,-1,2,1,0,1,2), 3, 3))