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. The heatmap produced performs supervised clustering, in the sense first described in Pollard & van der Laan (2008) <doi:10.2202/1544-6115.1404>.
heatmap_ic(x, ..., design, FDRcutoff = 0.05, type = c("top", "all"), 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. |
type |
A |
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) #