Heatmap of 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, as per Pollard & van der Laan (2008) <doi:10.2202/1544-6115.1404>.

heatmap_ic(x, ..., design, FDRcutoff = 0.25, type = c("top", "all"), top = 25)

## Arguments

x Object of class biotmle as produced by an appropriate call to biomarkertmle. additional arguments passed to superheat::superheat as necessary A vector giving the contrast to be displayed in the heatmap. Cutoff to be used in controlling the False Discovery Rate. A character describing whether to plot only a top number (as defined by FDR-corrected p-value) of biomarkers or all biomarkers. Number of identified biomarkers to plot in the heatmap.

## Value

heatmap (from superheat) using hierarchical clustering to plot the changes in the variable importance measure for all subjects across a specified top number of biomarkers.

## Examples

if (FALSE) {
library(dplyr)
library(biotmleData)
library(SummarizedExperiment)

data.frame() %>%
mutate(age = as.numeric(age > median(age))) %>%
DataFrame()

biomarkerTMLEout <- biomarkertmle(