BootKNN {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))