tlmixture is an R package to construct mixtures of groups of correlated exposures (treatments) and estimate the relationship between the mixture and an outcome.
You can install the development version of tlmixture from GitHub:
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("ck37/tlmixture")
This is a simple example which shows how to use some basic function arguments.
library(tlmixture)
# Basic example code
result =
tlmixture(
# Dataframe containing outcome, exposures, and adjustment variables.
data,
# Name of the outcome variable.
outcome = "y",
# Vector of exposure names (single group), or a list with separate vectors per group.
exposures = c("exposure1", "exposure2", "exposure3")
# This will evaluate mixtures at low/medium/high levels.
quantiles_mixtures = 3,
# This SuperLearner library will be used for propensity too.
estimator_outcome = c("SL.mean", "SL.glmnet", "SL.ranger")
# How many CV-TMLE folds to use; more is generally better, but slower to compute.
folds_cvtmle = 3)
# Review parameter estimates and confidence intervals.
result$combined$results