BootKNN: Bootstrap with kNN

In shipunov: Miscellaneous Functions from Alexey Shipunov

Bootstrap with kNN

Description

How to bootstrap with kNN (and DNN)

Usage

BootKNN(data, classes, sub="none", nsam=4, nboot=1000, misclass=TRUE, method="knn", ...)

Arguments

data	Data frame to classify
classes	Character vector of class names
sub	Subsample to use (see example)
nsam	Number of training items from each level of grouping factor, default 4
nboot	Number of iterations
misclass	Calculate misclassification table?
method	Either "knn" (class::knn()) or "dnn" (shipunov::Dnn())
...	Further arguments to method functions

Details

This function samples equal numbers ('nsam') of training items from each level of grouping factor.

It also allows to use subset of data which will be used for sub-sampling of training data.

Value

Returns all predictions as character matrix, each boot is a column

Author(s)

Alexey Shipunov

See Also

class::knn, Dnn

Examples

iris.sub <- 1:nrow(iris) %in% seq(1, nrow(iris), 5)
iris.bootknn <- BootKNN(iris[, -5], iris[, 5], sub=iris.sub)
## calculate and plot stability
st <- apply(iris.bootknn, 1, function(.x) var(as.numeric(as.factor(.x))))
plot(prcomp(iris[, -5])$x, col=iris$Species, pch=ifelse(st == 0, 19, 1))
## boot Dnn
BootKNN(iris[, -5], iris[, 5], nboot=50, method="dnn",
 k=1, FUN=function(.x) Gower.dist(.x))

shipunov documentation built on Feb. 16, 2023, 9:05 p.m.