# estimate.bas.glm: Obtaining Bayesian estimators of interest from a GLM model In aliaksah/EMJMCMC2016: EMJMCMC

## Usage

 `1` ```estimate.bas.glm(formula, data, family, prior, logn) ```

## Arguments

 `formula` a formula object for the model to be addressed `data` a data frame object containing variables and observations corresponding to the formula used `family` either poisson() or binomial(), that are currently adopted within this function `prior` aic.prior(),bic.prior() or ic.prior() are allowed `logn` log sample size

## Value

A list of

 `mlik` marginal likelihood of the model `waic` AIC model selection criterion `dic` BIC model selection criterion `summary.fixed\$mean` a vector of posterior modes of the parameters

 ```1 2 3 4 5 6 7 8 9``` ```X4= as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = runif(n = 50*1000,0,1)),dim = c(1000,50))) Y4=rnorm(n = 1000,mean = 1+7*(X4\$V4*X4\$V17*X4\$V30*X4\$V10)+7*(((X4\$V50*X4\$V19*X4\$V13*X4\$V11)>0)) + 9*(X4\$V37*X4\$V20*X4\$V12)+ 7*(X4\$V1*X4\$V27*X4\$V3) +3.5*(X4\$V9*X4\$V2) + 6.6*(X4\$V21*X4\$V18) + 1.5*X4\$V7 + 1.5*X4\$V8,sd = 1) X4\$Y4=Y4 data.example = as.data.frame(X4) data.example\$Y4=as.integer(data.example\$Y>mean(data.example\$Y)) formula1 = as.formula(paste(colnames(X4)[51],"~ 1 +",paste0(colnames(X4)[-c(51)],collapse = "+"))) estimate.bas.glm(formula = formula1, data = data.example,prior = aic.prior(), logn=47, family = binomial()) ```