Performs variance shrinkage via application of an empirical Bayes procedure (of LIMMA) on the observed data after a transformation moving the data to influence function space, based on the average treatment effect parameter.
modtest_ic(biotmle, adjust = "BH", pval_type = c("normal", "logistic"), ...)
biotmle 


adjust  the multiple testing correction to be applied to pvalues that are generated from the moderated tests. The recommended (default) method is that of Benjamini and Hochberg. See topTable for a list of appropriate methods. 
pval_type  The reference distribution to be used for computing the pvalue. Those based on the normal approximation tend to provide misleading inference when working with moderately sized (finite) samples. Use of the logistic distribution has been found to empirically improve performance in settings where multiple hypothesis testing is a concern. 
...  Other arguments passed to 
biotmle
object containing the results of applying both
lmFit
and topTable
.
library(dplyr) library(biotmleData) library(SuperLearner) library(SummarizedExperiment) data(illuminaData) colData(illuminaData) < colData(illuminaData) %>% data.frame() %>% dplyr::mutate(age = as.numeric(age > median(age))) %>% DataFrame() benz_idx < which(names(colData(illuminaData)) %in% "benzene") biomarkerTMLEout < biomarkertmle( se = illuminaData[1:2, ], varInt = benz_idx, bppar_type = BiocParallel::SerialParam(), g_lib = c("SL.mean", "SL.glm"), Q_lib = c("SL.mean", "SL.glm") )#>    0%  ===================================  50%  ====================================================================== 100% #>limmaTMLEout < modtest_ic(biotmle = biomarkerTMLEout)