FSmethod {HPiP}R Documentation

Feature Selection via Matrix Correlation and Recursive Feature Elimination (RFE)

Description

This function performs feature selections via two approaches

Usage

FSmethod(
  x,
  type = c("cor", "rfe", "both"),
  cor.cutoff = 0.7,
  resampling.method = "cv",
  iter = 2,
  repeats = 3,
  metric = "Accuracy",
  verbose = TRUE
)

Arguments

x

A data.frame containing protein-protein interactions, class labels and features.

type

The feature selection type, one or two of filter.corr and rfeFS.

cor.cutoff

Correlation coefficient cutoff used for filtering. See filter.corr for more details.

resampling.method

The resampling method for RFE :'boot', 'boot632', optimism_boot',boot_all', 'cv', 'repeatedcv', 'LOOCV', 'LGOCV';defaults to cv. See rfeFS and rfeControl for more details.

iter

Number of partitions for cross-validation; defaults to 2. See rfeFS and rfeControl for more details.

repeats

For repeated k-fold cross validation only; defaults to 3.See rfeFS and rfeControl for more details.

metric

A string that specifies what summary metric will be used to select the optimal feature ; default to ROC.See rfeFS and rfe for more details.

verbose

Make the output verbose.See rfeFS and rfeControl for more details.

Details

FSmethod

Value

If the type set to filter.corr , the output includes the following elements:

If the type set to rfeFS , the output includes the following elements:

If type set to both the output includes the following elements:

Author(s)

Matineh Rahmatbakhsh, matinerb.94@gmail.com.

Examples

data('example_data')
x <- na.omit(example_data)
s <- FSmethod(x, type = 'both',
cor.cutoff = 0.7, resampling.method = "repeatedcv",
iter = 5, repeats = 3, metric = "ROC", verbose = TRUE)

[Package HPiP version 1.0.0 Index]