estimate.logic.lm {EMJMCMC} | R Documentation |
Obtaining Bayesian estimators of interest from an LM model for the logic regression case
estimate.logic.lm(formula, data, n, m, r = 1)
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 |
marginal likelihood of the model
AIC model selection criterion
BIC model selection criterion
a vector of posterior modes of the parameters
BAS::bayesglm.fit, estimate.logic.glm
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)