| Title: | Weighted Quantile Sum Regression |
|---|---|
| Description: | Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights. |
| Authors: | Jenna Czarnota, David Wheeler |
| Maintainer: | Jenna Czarnota <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.0.1 |
| Built: | 2024-12-23 06:18:10 UTC |
| Source: | CRAN |
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
The DESCRIPTION file:
| Package: | wqs |
| Type: | Package |
| Title: | Weighted Quantile Sum Regression |
| Version: | 0.0.1 |
| Date: | 2015-10-05 |
| Author: | Jenna Czarnota, David Wheeler |
| Maintainer: | Jenna Czarnota <[email protected]> |
| Description: | Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights. |
| Depends: | R (>= 3.2.1) |
| Imports: | Rsolnp, glm2 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
| NeedsCompilation: | no |
| Packaged: | 2015-10-05 19:29:59 UTC; Jenna |
| Repository: | CRAN |
| Date/Publication: | 2015-10-05 22:13:29 |
Index of help topics:
WQSdata Simulated data to test WQS wqs-package Weighted Quantile Sum Regression wqs.est Weighted Quantile Sum Regression
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.
Jenna Czarnota, David Wheeler
Maintainer: Jenna Czarnota <[email protected]>
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
data(WQSdata) y.train <- WQSdata[,'y'] x.train <- WQSdata[,-10] output <- wqs.est(y.train, x.train, B = 10)data(WQSdata) y.train <- WQSdata[,'y'] x.train <- WQSdata[,-10] output <- wqs.est(y.train, x.train, B = 10)
This function fits a weighted quantile sum regression model.
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)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)
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 |
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 |
Jenna Czarnota, David Wheeler
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
data(WQSdata) y.train <- WQSdata[,'y'] x.train <- WQSdata[,-10] output <- wqs.est(y.train, x.train, B = 10)data(WQSdata) y.train <- WQSdata[,'y'] x.train <- WQSdata[,-10] output <- wqs.est(y.train, x.train, B = 10)
Correlation and concentration patterns were loosely based on NHL data.
data("WQSdata")data("WQSdata")
A data frame with 1000 observations on the following 10 variables.
X1a numeric vector
X2a numeric vector
X3a numeric vector
X4a numeric vector
X5a numeric vector
X6a numeric vector
X7a numeric vector
X8a numeric vector
X9a numeric vector
ya numeric vector; the outcome variable
Correlation and concentration patterns were loosely based on NHL data.
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
data(WQSdata)data(WQSdata)