Introduction
qgcomp is a package to implement g-computation for
analyzing the effects of exposure mixtures. Quantile g-computation
yields estimates of the effect of increasing all exposures by one
quantile, simultaneously. This, it estimates a “mixture effect” useful
in the study of exposure mixtures such as air pollution, diet, and water
contamination.
Help for this package is available in the help files for individual
functions, or in the package vignettes.
The basic vignette can be viewed in R/Rstudio by running the
following
vignette("qgcomp-basic-vignette", "qgcomp")
The basic vignette covers:
- Introduction
- How to use the
qgcomp package
- Example 1: linear model
- Example 2: conditional odds ratio, marginal odds ratio in a logistic
model(#ex-logistic)
- Example 3: adjusting for covariates, plotting estimates
- Example 4: non-linearity (and non-homogeneity)
- Example 5: comparing model fits and further exploring
non-linearity
- Example 6: miscellaneous other ways to allow non-linearity
- FAQ
The advanced vignette can be viewed in R/Rstudio by running the
following
vignette("qgcomp-advanced-vignette", "qgcomp")
The advanced vignette covers:
- Example 7: time-to-event analysis and parallel processing
- Example 8: clustering
- Example 9: partial effects
- Example 10: multinomial outcomes
- Example 11: sample weighting from, e.g. NHANES
- Missing data, limits of detection and multiple imputation