estimate.speedglm {EMJMCMC}

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)

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

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

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)