EMJMCMC (version 1.5.0)

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

Description

Obtaining Bayesian estimators of interest from a GLM model

Usage

estimate.speedglm(formula, data, family, prior, logn)

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

family

distribution family foe the responses

prior

either "AIC" or "BIC"

logn

log sample size

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) +
    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)