bws_wrapper {mpower}

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.