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

In aliaksah/EMJMCMC2016: EMJMCMC

Obtaining Bayesian estimators of interest from a GLM model

Usage

estimate.speedglm(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	distribution family foe the responces
prior	either "AIC" or "BIC"
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

speedglm::speedglm.wfit

Examples

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)
formula1 = as.formula(paste(colnames(X4)[51],"~ 1 +",paste0(colnames(X4)[-c(51)],collapse = "+")))

estimate.speedglm(formula = formula1, data = data.example,prior = "BIC", logn=log(47), family = gaussian())

aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.