wqs (version 0.0.1)

wqs.est: 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

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)