wqs.est: Weighted Quantile Sum Regression

In wqs: Weighted Quantile Sum Regression

Description

This function fits a weighted quantile sum regression model.

Usage

wqs.est(y.train, x.train, z.train = NULL, y.valid = y.train, x.valid = x.train, 
z.valid = z.train, n.quantiles = 4, B = 100, b1.pos = TRUE)

Arguments

y.train	vector of the continuous explanatory variable from training data
x.train	matrix of explanatory variables (to be combined into an index) from training data
z.train	vector or matrix of covariates from training data
y.valid	vector of the continuous explanatory variable from validation data
x.valid	matrix of explanatory variables (to be combined into an index) from validation data
z.valid	vector or matrix of covariates from validation data
n.quantiles	number of quantiles to be used (needs to be between 2 and 10)
B	number of bootstrap samples to be used in estimation (needs to be greater than 1)
b1.pos	TRUE if the index is expected to be positively related to the outcome

Value

A list with the following items:

q.train	matrix of quantiles for training data
q.valid	matrix of quantiles for validation data
wts.matrix	matrix of estimated weights; each row corresponds to a bootstrap sample
weights	final estimated weights used in calculating the WQS index
WQS	weighted quantile sum estimate based on calculated weights
fit	WQS model fit to validation data

Author(s)

Jenna Czarnota, David Wheeler

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.