randomsample {nestedcv}

Oversampling and undersampling

Description

Random oversampling of the minority group(s) or undersampling of the majority group to compensate for class imbalance in datasets.

Usage

randomsample(y, x, minor = NULL, major = 1, yminor = NULL)

Arguments

y

Vector of response outcome as a factor

x

Matrix of predictors

minor

Amount of oversampling of the minority class. If set to NULL then all classes will be oversampled up to the number of samples in the majority class. To turn off oversampling set minor = 1.

major

Amount of undersampling of the majority class

yminor

Optional character value specifying the level in y which is to be oversampled. If NULL, this is set automatically to the class with the smallest sample size.

Details

minor < 1 and major > 1 are ignored.

Value

List containing extended matrix x of synthesised data and extended response vector y

Examples

## Imbalanced dataset
set.seed(1, "L'Ecuyer-CMRG")
x <- matrix(rnorm(150 * 2e+04), 150, 2e+04)  #' predictors
y <- factor(rbinom(150, 1, 0.2))  #' imbalanced binary response
table(y)

## first 30 parameters are weak predictors
x[, 1:30] <- rnorm(150 * 30, 0, 1) + as.numeric(y)*0.5

## Balance x & y outside of CV loop by random oversampling minority group
out <- randomsample(y, x)
y2 <- out$y
x2 <- out$x
table(y2)

## Nested CV glmnet with unnested balancing by random oversampling on
## whole dataset
fit1 <- nestcv.glmnet(y2, x2, family = "binomial", alphaSet = 1,
                      cv.cores=2,
                      filterFUN = ttest_filter)
fit1$summary

## Balance x & y outside of CV loop by random oversampling minority group
out <- randomsample(y, x, minor=1, major=0.4)
y2 <- out$y
x2 <- out$x
table(y2)

## Nested CV glmnet with unnested balancing by random undersampling on
## whole dataset
fit1b <- nestcv.glmnet(y2, x2, family = "binomial", alphaSet = 1,
                       cv.cores=2,
                       filterFUN = ttest_filter)
fit1b$summary

## Balance x & y outside of CV loop by SMOTE
out <- smote(y, x)
y2 <- out$y
x2 <- out$x
table(y2)

## Nested CV glmnet with unnested balancing by SMOTE on whole dataset
fit2 <- nestcv.glmnet(y2, x2, family = "binomial", alphaSet = 1,
                      cv.cores=2,
                      filterFUN = ttest_filter)
fit2$summary

## Nested CV glmnet with nested balancing by random oversampling
fit3 <- nestcv.glmnet(y, x, family = "binomial", alphaSet = 1,
                      cv.cores=2,
                      balance = "randomsample",
                      filterFUN = ttest_filter)
fit3$summary
class_balance(fit3)

## Plot ROC curves
plot(fit1$roc, col='green')
lines(fit1b$roc, col='red')
lines(fit2$roc, col='blue')
lines(fit3$roc)
legend('bottomright', legend = c("Unnested random oversampling", 
                                 "Unnested SMOTE",
                                 "Unnested random undersampling",
                                 "Nested balancing"), 
       col = c("green", "blue", "red", "black"), lty=1, lwd=2)