fin_wrapper: Fits a Bayesian factor model with interactions

In phuchonguyen/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.

phuchonguyen/mpower documentation built on Oct. 2, 2022, 7:57 p.m.