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

In aliaksah/EMJMCMC2016: EMJMCMC

Obtaining Bayesian estimators of interest from a GLM model in a logic regression context

Usage

estimate.logic.glm(formula, data, family, n, m, r = 1)

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
n	sample size
m	total number of input binary leaves
r	omitted

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, estimate.logic.lm

Examples

X1=as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = 0.3),dim = c(1000,50)))
Y1=-0.7+1*((1-X1$V1)*(X1$V4)) + 1*(X1$V8*X1$V11)+1*(X1$V5*X1$V9)
X1$Y1=round(1.0/(1.0+exp(-Y1)))

formula1 = as.formula(paste(colnames(X1)[51],"~ 1 +",paste0(colnames(X1)[-c(51)],collapse = "+")))

estimate.logic.glm(formula = formula1, data = X1,family = binomial(),n = 1000, m = 50)

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