wqs-package: Weighted Quantile Sum Regression

In wqs: Weighted Quantile Sum Regression

Description

Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.

Details

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This package performs weighted quantile sum (WQS) regression, by fitting a WQS regression model for a continuous outcome variable. The components (e.g. chemicals) to be combined into an index are scored into quantiles and then used in the estimation of empirically derived weights and a final WQS index through bootstrap sampling. The weights are constrained to sum to 1 and be between 0 and 1, and can be used to identify important (highly weighted) components and those with no association with outcome (components recieving zero or negligable weight). Inference is constrained in a single direction and the index is interpretable as a measure of the mixture effect.

Author(s)

Jenna Czarnota, David Wheeler

Maintainer: Jenna Czarnota <[e-mail]>

References

Carrico C, Gennings C, Wheeler D, Factor-Litvak P. Characterization of a weighted quantile sum regression for highly correlated data in a risk analysis setting. J Biol Agricul Environ Stat. 2014:1-21. ISSN: 1085-7117. DOI: 10.1007/ s13253-014-0180-3. http://dx.doi.org/10.1007/s13253-014-0180-3.

Czarnota J, Gennings C, Colt JS, De Roos AJ, Cerhan JR, Severson RK, Hartge P, Ward MH, Wheeler D. 2015. Analysis of environmental chemical mixtures and non-Hodgkin lymphoma risk in the NCI-SEER NHL study. Environmental Health Perspectives, DOI:10.1289/ehp.1408630.

Czarnota J, Gennings C, Wheeler D. 2015. Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk. Cancer Informatics, 2015:14(S2) 159-171 DOI: 10.4137/CIN.S17295

Examples

data(WQSdata)
y.train <- WQSdata[,'y']
x.train <- WQSdata[,-10]
output <- wqs.est(y.train, x.train, B = 10)

Example output

wqs documentation built on May 2, 2019, 8:30 a.m.