fin_wrapper: Fits a Bayesian factor model with interactions

In mpower: Power Analysis via Monte Carlo Simulation for Correlated Data

Fits a Bayesian factor model with interactions

Description

Fits a Bayesian factor model with interactions

Usage

fin_wrapper(y, x, args = list(nrun = 2000))

Arguments

y	A vector of outcome
x	A matrix of predictors
args	A list of arguments see R function ‘infinitefactor::interactionDL()' in ’infinitefactor' package.

Value

A list of vectors whose values are between 0 and 1

beta	The smallest posterior probability of the coefficients being to one side of zero for either main effect or interaction: min(Pr(beta >0), Pr(beta<0)).
linear_beta	The smaller of posterior probability of the main effects being to one side of zero.
interact_beta	Same as linear_beta but for pair-wise interactions.
time	elapsed time to fit the model.

Reference

Ferrari F, Dunson DB (2020). “Bayesian factor analysis for inference on interactions.”Journal of the American Statistical Association, 0(0), 1–12.

mpower documentation built on Sept. 21, 2022, 9:13 a.m.