knnCMA: Nearest Neighbours
In CMA: Synthesis of microarray-based classification

Description Usage Arguments Value Note Author(s) References See Also Examples

Ordinary k nearest neighbours algorithm from the very fast implementation in the package class.

For S4 method information, see knnCMA-methods.

1	knnCMA(X, y, f, learnind, models=FALSE, ...)

`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.
`models`	a logical value indicating whether the model object shall be returned
`...`	Further arguments to be passed to `knn` from the package `class`, in particular the number of nearest neighbours to use (argument `k`).

An object of class cloutput.

Class probabilities are not returned. For a probabilistic variant of knn, s. pknnCMA.

Martin Slawski ms@cs.uni-sb.de

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

Ripley, B.D. (1996)

Pattern Recognition and Neural Networks.

Cambridge University Press

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

### 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 k-nearest neighbours
result <- knnCMA(X=golubX, y=golubY, learnind=learnind, k = 3)
### show results
show(result)
ftable(result)
### multiclass example:
### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run knn
result <- knnCMA(X=khanX, y=khanY, learnind=learnind, k = 5)
### show results
show(result)
ftable(result)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

binary Classification with k nearest neighbours
number of predictions: 13
number of missclassifications:  1 
missclassification rate:  0.077 
sensitivity: 1 
specificity: 0.875 
    predicted
true 0 1
   0 7 1
   1 0 5

multiclass Classification with k nearest neighbours
number of predictions: 21
number of missclassifications:  7 
missclassification rate:  0.333 
    predicted
true 0 1 2 3
   0 2 0 0 0
   1 2 5 0 2
   2 0 0 4 1
   3 0 2 0 3