EMJMCMC (version 1.5.0)

estimate.logic.lm: Obtaining Bayesian estimators of interest from an LM model for the logic regression case

Description

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

Usage

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

Value

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

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

See Also

BAS::bayesglm.fit, estimate.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)