plotExplanatoryHeatmap: Heatmap plot of explantory features

In jasenfinch/metabolyseR: Methods for Pre-Treatment, Data Mining and Correlation Analyses of Metabolomics Data

Heatmap plot of explantory features

Description

Plot a heatmap of explanatory features.

Usage

plotExplanatoryHeatmap(x, ...)

## S4 method for signature 'Univariate'
plotExplanatoryHeatmap(
  x,
  threshold = 0.05,
  title = "",
  distanceMeasure = "euclidean",
  clusterMethod = "ward.D2",
  featureNames = TRUE,
  dendrogram = TRUE,
  featureLimit = Inf,
  ...
)

## S4 method for signature 'RandomForest'
plotExplanatoryHeatmap(
  x,
  metric = "false_positive_rate",
  threshold = 0.05,
  title = "",
  distanceMeasure = "euclidean",
  clusterMethod = "ward.D2",
  featureNames = TRUE,
  dendrogram = TRUE,
  featureLimit = Inf,
  ...
)

## S4 method for signature 'list'
plotExplanatoryHeatmap(
  x,
  threshold = 0.05,
  distanceMeasure = "euclidean",
  clusterMethod = "ward.D2",
  featureNames = TRUE,
  featureLimit = Inf
)

## S4 method for signature 'Analysis'
plotExplanatoryHeatmap(
  x,
  threshold = 0.05,
  distanceMeasure = "euclidean",
  clusterMethod = "ward.D2",
  featureNames = TRUE,
  featureLimit = Inf
)

Arguments

x	object of class Univariate, RandomForest or Analysis
...	arguments to pass to method explanatoryFeatures()
threshold	score threshold to use for specifying explanatory features
title	plot title
distanceMeasure	distance measure to use for clustering. See details.
clusterMethod	clustering method to use. See details
featureNames	should feature names be plotted?
dendrogram	TRUE/FALSE. Should the dendrogram be plotted?
featureLimit	The maximum number of features to plot
metric	importance metric on which to retrieve explanatory features

Details

Distance measures can be one of any that can be used for the method argument of dist().

Cluster methods can be one of any that can be used for the method argument of hclust().

Examples

library(metaboData)
x <- analysisData(data = abr1$neg[,200:300],info = abr1$fact)

## random forest classification example
random_forest <- randomForest(x,cls = 'day')

plotExplanatoryHeatmap(random_forest)

## random forest regression example
random_forest <- randomForest(x,cls = 'injorder')

plotExplanatoryHeatmap(random_forest,metric = '%IncMSE',threshold = 2)

jasenfinch/metabolyseR documentation built on Sept. 18, 2023, 1:25 a.m.