% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{heatmap_ic}
\alias{heatmap_ic}
\title{Heatmap for class biotmle}
\usage{
heatmap_ic(x, ..., design, FDRcutoff = 0.25, type = c("top", "all"), top = 25)
}
\arguments{
\item{x}{Object of class \code{biotmle} as produced by an appropriate call
to \code{biomarkertmle}.}

\item{...}{additional arguments passed to \code{superheat::superheat} as
necessary}

\item{design}{A vector giving the contrast to be displayed in the heatmap.}

\item{FDRcutoff}{Cutoff to be used in controlling the False Discovery Rate.}

\item{type}{A \code{character} describing whether to plot only a top number
(as defined by FDR-corrected p-value) of biomarkers or all biomarkers.}

\item{top}{Number of identified biomarkers to plot in the heatmap.}
}
\value{
heatmap (from \pkg{superheat}) using hierarchical clustering to plot
 the changes in the variable importance measure for all subjects across a
 specified top number of biomarkers.
}
\description{
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>.
}
\examples{
\dontrun{
library(dplyr)
library(biotmleData)
library(SummarizedExperiment)
data(illuminaData)

colData(illuminaData) <- colData(illuminaData) \%>\%
  data.frame() \%>\%
  mutate(age = as.numeric(age > median(age))) \%>\%
  DataFrame()
benz_idx <- which(names(colData(illuminaData)) \%in\% "benzene")

biomarkerTMLEout <- biomarkertmle(
  se = illuminaData,
  varInt = benz_idx,
  bppar_type = BiocParallel::SerialParam(),
  g_lib = c("SL.mean", "SL.glm"),
  Q_lib = c("SL.mean", "SL.glm")
)

limmaTMLEout <- modtest_ic(biotmle = biomarkerTMLEout)

heatmap_ic(x = limmaTMLEout, design = design, FDRcutoff = 0.05, top = 10)
}
}