pknnCMA: Probabilistic Nearest Neighbours

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Nearest neighbour variant that replaces the simple voting scheme by a weighted one (based on euclidean distances). This is also used to compute class probabilities.

For S4 class information, see pknnCMA-methods.

Usage

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pknnCMA(X, y, f, learnind, beta = 1, k = 1, models=FALSE, ...)

Arguments

X

Gene expression data. Can be one of the following:

  • A matrix. Rows correspond to observations, columns to variables.

  • A data.frame, when f is not missing (s. below).

  • An object of class ExpressionSet.

y

Class labels. Can be one of the following:

  • A numeric vector.

  • A factor.

  • A character if X is an ExpressionSet that specifies the phenotype variable.

  • missing, if X is a data.frame and a proper formula f is provided.

WARNING: The class labels will be re-coded to range from 0 to K-1, where K is the total number of different classes in the learning set.

f

A two-sided formula, if X is a data.frame. The left part correspond to class labels, the right to variables.

learnind

An index vector specifying the observations that belong to the learning set. Must not be missing for this method.

beta

Slope parameter for the logistic function which is used for the computation of class probabilities. The default value (1) need not produce reasonable results and can produce warnings.

k

Number of nearest neighbours to use.

models

a logical value indicating whether the model object shall be returned

...

Currently unused argument.

Details

The algorithm is as follows:

Value

An object of class cloutput.

Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

See Also

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA

Examples

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### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression from first 10 genes
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run probabilistic k-nearest neighbours
result <- pknnCMA(X=golubX, y=golubY, learnind=learnind, k = 3)
### show results
show(result)
ftable(result)
plot(result)

chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.