print.ssvs_mi_summary

internal

Functions for performing stochastic search variable selection (SSVS)
for binary and continuous outcomes and visualizing the results.
SSVS is a Bayesian variable selection method used to estimate the probability
that individual predictors should be included in a regression model.
Using MCMC estimation, the method samples thousands of regression models
in order to characterize the model uncertainty regarding both the predictor
set and the regression parameters. For details see Bainter, McCauley, Wager,
and Losin (2020) Improving practices for selecting a subset of important
predictors in psychology: An application to predicting pain, Advances in
Methods and Practices in Psychological Science 3(1), 66-80
<DOI:10.1177/2515245919885617>.

Sierra Bainter

SSVS

Functions for Stochastic Search Variable Selection (SSVS)

Thomas McCauley

Mahmoud Fahmy

Dean Attali

print.ssvs_mi_summary function

Print the summary of ssvs_mi — print.ssvs_mi_summary

Print the summary of ssvs_mi

print.ssvs_mi_summary: Print the summary of ssvs_mi

Description

Usage

Arguments