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-Based Estimation.
biomarkertmle(se, varInt, ngscounts = FALSE, parallel = TRUE, bppar_type = NULL, future_param = NULL, family = "gaussian", subj_ids = NULL, g_lib = c("SL.glm", "SL.randomForest", "SL.nnet", "SL.polymars", "SL.mean"), Q_lib = c("SL.glm", "SL.randomForest", "SL.nnet", "SL.mean"))
se |
(SummarizedExperiment) - containing expression or next-generation sequencing data in the "assays" slot and a matrix of phenotype-level data in the "colData" slot. |
varInt |
(numeric) - indicating the column of the design matrix corresponding to the treatment or outcome of interest (in the "colData" slot of the "se" argument above). |
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 microarray-type platforms. |
parallel |
(logical) - whether or not to use parallelization in the
estimation procedure. Invoking parallelization happens through a
combination of calls 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 |
family |
(character) - specification of error family: "binomial" or "gaussian". |
subj_ids |
(numeric vector) - subject IDs to be passed directly to the same subject should have the exact same numerical identifier; coerced to numeric if not provided in the appropriate form. |
g_lib |
(char vector) - library of learning algorithms to be used in fitting the "g" step of the standard TMLE procedure. |
Q_lib |
(char vector) - library of learning algorithms to be used in fitting the "Q" step of the standard TMLE procedure. |
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, family = "gaussian", g_lib = c("SL.mean", "SL.glm"), Q_lib = "SL.mean" )