biomarkertmle {biotmle} | R Documentation |
Computes the causal target parameter defined as the difference between the biomarker expression values under treatment and those same values under no treatment, using Targeted Minimum Loss Estimation.
biomarkertmle(se, varInt, normalized = TRUE, ngscounts = FALSE, parallel = TRUE, bppar_type = NULL, future_param = NULL, subj_ids = NULL, cv_folds = 5, g_lib = c("SL.mean", "SL.glm", "SL.glmnet", "SL.earth"), Q_lib = c("SL.mean", "SL.glm", "SL.randomForest"), ...)
se |
A |
varInt |
A |
normalized |
(logical) - whether the data included in the |
ngscounts |
(logical) - whether the data are counts generated from a next-generation sequencing (NGS) experiment (e.g., RNA-seq). The default setting assumes continuous expression measures as generated by platforms that are microarray-type (i.e., so-called "targeted" assays). |
parallel |
(logical) - whether or not to use parallelization in the
estimation procedure. Invoking parallelization happens through a
combination of calls to future and BiocParallel. If this
argument is set to |
bppar_type |
(character) - specifies the type of backend to be used with
the parallelization invoked by |
future_param |
(character) - specifies the type of parallelization to be
invoked when using futures for evaluation. For a list of the available
types, please consult the documentation for |
subj_ids |
(numeric vector) - subject IDs to be passed directly to
the same subject should have the exact same numerical identifier; coerced to
class |
cv_folds |
A |
g_lib |
(char vector) - library of learning algorithms to be used in fitting the propensity score E[A | W] (the nuisance parameter denoted "g" in the literature on targeted minimum loss-based estimation). |
Q_lib |
(char vector) - library of learning algorithms to be used in fitting the outcome regression E[Y | A, W] (the nuisance parameter denoted "Q" in the literature on targeted minimum loss-based estimation). |
... |
Additional arguments to be passed to |
S4 object of class biotmle
, generated by sub-classing
SummarizedExperiment
, with additional slots containing tmleOut
and call
, among others, containing TMLE-based estimates of the
relationship between a biomarker and exposure or outcome variable and the
original call to this function (for user reference), respectively.
library(dplyr) library(biotmleData) data(illuminaData) library(SummarizedExperiment) "%ni%" <- Negate("%in%") colData(illuminaData) <- colData(illuminaData) %>% data.frame() %>% dplyr::mutate(age = as.numeric(age > median(age))) %>% DataFrame() varInt_index <- which(names(colData(illuminaData)) %in% "benzene") biomarkerTMLEout <- biomarkertmle( se = illuminaData[1:2, ], varInt = varInt_index, parallel = FALSE, g_lib = c("SL.mean", "SL.glm"), Q_lib = "SL.glm" ) #