CVRandomForestClassificationMatrixForPheatmap: Generate a random forest model under cross validation (CV)...

In yhhc2/machinelearnr: Package with various functions for preprocessing, clustering, and classification

Description

CVPredictionsRandomForest() is used on each subset of the data. For each subset of the data: A random forest model with a specified fold CV is generated. This random forest model is used to make a single column in the final matrix which can be used to generate a pheatmap. The inputted data should already have the rows shuffled. If LOOCV should be done on each subsetted data, then this can be indicated by using -1 for the number.of.folds.

Usage

CVRandomForestClassificationMatrixForPheatmap(
  input.data,
  factor.name.for.subsetting,
  name.of.predictors.to.use,
  target.column.name,
  seed,
  percentile.threshold.to.keep,
  number.of.folds
)

Arguments

input.data	A dataframe which should already have the rows shuffled.
factor.name.for.subsetting	String to specify name of column to use for subsetting. The column should be a factor. Each column of the generated pheatmap will correspond to a level in the factor.
name.of.predictors.to.use	A vector of strings to specify name of columns to use as predictors for random forest model. Each column should be numeric.
target.column.name	A string to specify the column with values the random forest model is trying to predict for. The column should be a factor.
seed	A number to set for random number generation.
percentile.threshold.to.keep	A number from 0-1 indicating the percentile to use for feature selection. This is used by the CVPredictionsRandomForest() function.
number.of.folds	An integer to specify the fold for CV. If This number is set to -1, then the function will use LOOCV for each subset data set.

Value

A list with three objects:

1. A numerical matrix that can be used for pheatmap generation.

2. A list of subset data sets for each column in the heatmap matrix.

3. A list of vectors containing the predictions for each column in the heatmap matrix. These values are used to calculate the MCC.

See Also

Other Classification functions: CVPredictionsRandomForest(), GenerateExampleDataMachinelearnr(), LOOCVPredictionsRandomForestAutomaticMtryAndNtree(), LOOCVRandomForestClassificationMatrixForPheatmap(), RandomForestAutomaticMtryAndNtree(), RandomForestClassificationGiniMatrixForPheatmap(), RandomForestClassificationPercentileMatrixForPheatmap(), eval.classification.results(), find.best.number.of.trees()

Examples

example.data <- GenerateExampleDataMachinelearnr()

set.seed(1)
example.data.shuffled <- example.data[sample(nrow(example.data)),]

result.two.fold.CV <- CVRandomForestClassificationMatrixForPheatmap(
input.data = example.data.shuffled,
factor.name.for.subsetting = "sep.xy.ab",
name.of.predictors.to.use = c("x", "y", "a", "b"),
target.column.name = "actual",
seed = 2,
percentile.threshold.to.keep = 0.5,
number.of.folds = 2)

matrix.for.pheatmap <- result.two.fold.CV[[1]]

pheatmap_RF <- pheatmap::pheatmap(matrix.for.pheatmap, fontsize_col = 12, fontsize_row=12)

yhhc2/machinelearnr documentation built on Dec. 23, 2021, 7:19 p.m.