knnB {MLInterfaces}R Documentation

An interface to various machine learning methods for exprSets

Description

This document describes a family of wrappers of calls to machine learning classifiers distributed through various R packages. This particular document concerns the classifiers for which training-vs-test set application makes sense.

For example, knnB is a wrapper for a call to knn for objects of class exprSet. These interfaces, of the form [f]B provide a common calling sequence and common return value for machine learning code in function [f].

For details on the additional arguments that may be passed to any covered machine learning function f, check the manual page for that function. This will require loading the package in which f is found.

Usage

knnB(exprObj, classifLab, trainInd, k = 1, l = 1, prob = TRUE,
  use.all = TRUE, metric = "euclidean") 

Arguments

exprObj An instance of the exprset class.
classifLab A vector of class labels.
trainInd integer vector: Which elements are the training set.
k The number of nearest neighbors.
l See knn for a complete description.
prob See knn for a complete description.
use.all See knn for a complete description.
metric See knn for a complete description.

Details

See knn for a complete description of parameters to and details of the k-nearest neighbor procedure in the class package.

Value

An object of class classifOutput-class.

Author(s)

Jess Mar, VJ Carey <stvjc@channing.harvard.edu>

See Also

ldaB

Examples

# access and trim an exprSet
library(golubEsets)
data(golubMerge)
smallG <- golubMerge[1:60,]
# set a PRNG seed for reproducibilitiy
set.seed(1234) # needed for nnet initialization
# now run the classifiers
knnB( smallG, "ALL.AML", 1:40 )
nnetB( smallG, "ALL.AML", 1:40, size=5, decay=.01 )
lvq1B( smallG, "ALL.AML", 1:40 )
naiveBayesB( smallG, "ALL.AML", 1:40 )
svmB( smallG, "ALL.AML", 1:40 )
baggingB( smallG, "ALL.AML", 1:40 )
ipredknnB( smallG, "ALL.AML", 1:40 )
sldaB( smallG, "ALL.AML", 1:40 )
ldaB( smallG, "ALL.AML", 1:40 )
qdaB( smallG[1:10,], "ALL.AML", 1:40 )
pamrB( smallG, "ALL.AML", 1:40 )
rpartB( smallG, "ALL.AML", 1:35 )
randomForestB( smallG, "ALL.AML", 1:35 )
gbmB( smallG, "ALL.AML", 1:40, n.minobsinnode=3 , n.trees=6000)
if (require(LogitBoost)) logitboostB( smallG, "ALL.AML", 1:40, 200 ) # summarize won't work with polych
stat.diag.daB( smallG, "ALL.AML", 1:40 )
#
# illustrate the xval method
#
LOO1 <- xval(smallG, "ALL.AML", knnB, "LOO" ) # same as knn.cv
LOO2 <- xval(smallG, "ALL.AML", knnB, "FUN", 0:0, function(x,y,i) {
  (1:ncol(exprs(x)))[-i] }, niter=72 )
table(LOO1, LOO2)

[Package MLInterfaces version 1.0.10 Index]