print.pep: Printing object of class pep

In PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior

Printing object of class pep

Description

For each of the top models (shown in columns), the following information is printed: the model representation using variable inclusion indicators, its marginal likelihood (in log scale), the R2, the model dimension, the Bayes factor, posterior odds (comparison made with the highest posterior probability model) and posterior probability. An additional column with the posterior inclusion probabilities of the explanatory variables is also printed.

Usage

## S3 method for class 'pep'
print(
  x,
  n.models = 5,
  actual.PO = FALSE,
  digits = max(3L, getOption("digits") - 3L),
  ...
)

Arguments

x	An object of class pep (e.g., output of pep.lm).
n.models	Positive integer, the number of top models for which information is provided. Default value=5.
actual.PO	Logical, relevant for the MC3 algorithm. If TRUE then apart from the estimated posterior odds, the actual posterior odds of the MAP model versus the top models (i.e., ratios based on the marginal likelihood times prior probability) are also printed — which could be used as a convergence indicator of the algorithm. Default value=FALSE.
digits	Positive integer, the number of digits for printing numbers. Default value=max(3L, getOption("digits") - 3L).
...	Additional parameters to be passed to print.default.

Details

The number of models for which information is provided, is computed as the minimum between the number asked by the user and the number of models present in the object x.

Value

No return value, used for printing the results on the R console.

Examples

data(UScrime_data)
res <- pep.lm(y~.,data=UScrime_data)
print(res)

PEPBVS documentation built on April 3, 2025, 6:12 p.m.