bws_wrapper: Fits a Bayesian weighted sums

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

Fits a Bayesian weighted sums

Description

Fits a Bayesian weighted sums

Usage

bws_wrapper(y, x, args = list(iter = 2000))

Arguments

y	A vector of outcome
x	A matrix of predictors
args	A list of arguments see R 'bws::bws()“ function.

Value

A list

beta	The smaller posterior probability of the combined overall effect being to one side of zero: min(Pr(beta >0), Pr(beta<0)). The same for all predictor.
weights	The 95% CI of the contribution of each predictor to the overall effect. Between 0 and 1.
time	elapsed time to fit the model.

Reference

Hamra GB, MacLehose RF, Croen L, Kauffman EM, Newschaffer C (2021). “Bayesian weighted sums: a flexible approach to estimate summed mixture effects.” International Journal of Environmental Research and Public Health, 18(4), 1373.

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