Balances between negative and positive samples by oversampling.

Description

If negative samples are less than positive ones, more copies of the negative cases are added and vice versa.

Usage

compute.balanced(F_, L_)

Arguments

Details

Considerably unbalanced classes may be probabilistic for fitting some models.

Value

Returns a list of:

Author(s)

Habil Zare

References

"Statistical Analysis of Overfitting Features", manuscript in preparation.

See Also

FeaLect, train.doctor, doctor.validate, random.subset, compute.balanced,compute.logistic.score, ignore.redundant, input.check.FeaLect

Examples

library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
L <- as.numeric(mcl_sll[ ,1])	# The labels
names(L) <- rownames(F)
message(L)

balanced <- compute.balanced(F_=F, L_=L)
message(balanced$L_)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(FeaLect)
Loading required package: lars
Loaded lars 1.2

Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FeaLect/compute.balanced.Rd_%03d_medium.png", width=480, height=480)
> ### Name: compute.balanced
> ### Title: Balances between negative and positive samples by oversampling.
> ### Aliases: compute.balanced
> ### Keywords: regression multivariate classif models
> 
> ### ** Examples
> 
> library(FeaLect)
> data(mcl_sll)
> F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
> L <- as.numeric(mcl_sll[ ,1])	# The labels
> names(L) <- rownames(F)
> message(L)
1111111000000000000000
> 
> balanced <- compute.balanced(F_=F, L_=L)
> message(balanced$L_)
111111100000000000000011111111
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>