estimate.bas.glm: Obtaining Bayesian estimators of interest from a GLM model

Usage Arguments Value See Also Examples

View source: R/the_mode_jumping_package5.r View source: R/the_mode_jumping_package4.r View source: R/the_mode_jumping_package4.0.r View source: R/the_mode_jumping_package3.r View source: R/the_mode_jumping_package2.r View source: R/the_mode_jumping_package2.0.r

Usage

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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

See Also

BAS::bayesglm.fit

Examples

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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())

aliaksah/EMJMCMC2016 documentation built on July 19, 2019, 12:01 p.m.