| heatmap_ic {biotmle} | R Documentation |
Heatmap of the contributions of a select subset of biomarkers to the variable importance measure changes as assessed by influence curve-based estimation, across all subjects.
heatmap_ic(x, ..., design, FDRcutoff = 0.05, top = 25)
x |
object of class |
... |
additional arguments passed to |
design |
a vector providing the contrast to be displayed in the heatmap. |
FDRcutoff |
cutoff to be used in controlling the False Discovery Rate |
top |
number of identified biomarkers to plot in the heatmap |
heatmap (from the superheat package) using hierarchical clustering to plot the changes in the variable importance measure for all subjects across a specified top number of biomarkers.
library(dplyr)
library(biotmleData)
library(SummarizedExperiment)
data(illuminaData)
data(biomarkertmleOut)
colData(illuminaData) <- colData(illuminaData) %>%
data.frame %>%
dplyr::mutate(age = as.numeric(age > median(age))) %>%
DataFrame
varInt_index <- which(names(colData(illuminaData)) %in% "benzene")
designVar <- as.data.frame(colData(illuminaData))[, varInt_index]
design <- as.numeric(designVar == max(designVar))
limmaTMLEout <- modtest_ic(biotmle = biomarkerTMLEout)
heatmap_ic(x = limmaTMLEout, design = design, FDRcutoff = 0.05, top = 15)