estimate.logic.lm: Obtaining Bayesian estimators of interest from an LM model...

In aliaksah/EMJMCMC2016: EMJMCMC

Obtaining Bayesian estimators of interest from an LM model for the logic regression case

Usage

estimate.logic.lm(formula, data, 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
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, esimate.logic.glm

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


estimate.logic.lm(formula = formula1,data = X4,n = 1000, m = 50)

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