% 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)

}

}