MLearn-methods {MLInterfaces}R Documentation

Methods for Function MLearn in Package ‘MLInterfaces’

Description

Initial version of more lightweight interface; please do help(MLearn) for the more recent schema-based approach.

Methods

The parameters of the generic are formula, data, method, and trainInds.

The fundamental method employs a formula and a data.frame instance and applies a machine learning algorithm identified by method, specifying the training set indices for the training run. An instance of MLOutput-class is returned.

An adaptation allows an ExpressionSet instance to be bound to the data parameter. The ExpressionSet and phenoData will be converted to a data.frame instance using the internal es2df function, and this can be large. Typically the genes will be filtered before applying this procedure.

For this interface, one can obtain the training data confusion matrix using confuMatTrain. A slot predLabelsTr is populated for this purpose, and an extractor method exists.

Examples

library(MASS)
data(Pima.tr)
pm = MLearn(type~., data=Pima.tr, "lda", 1:150 )
confuMatTrain(pm)  # on training data
confuMat(pm)  # on held-out test data
#
pm2 = MLearn(type~., data=Pima.tr, "logistic", 1:150, mlSpecials=
  list(thresh=.2) )
confuMat(pm2)
#
library(golubEsets)
data(Golub_Merge)
rp = MLearn(ALL.AML~., Golub_Merge[1:200,], "rpart", 1:35 )
confuMat(rp)
sv = MLearn(ALL.AML~., Golub_Merge[1:200,], "svm", 1:35 )
confuMat(sv)
confuMatTrain(sv)
# illustrate real adaboost
rab = MLearn(ALL.AML~., Golub_Merge[1:200,], "RAB", 1:35, 
   maxiter=20, maxdepth=2)
confuMatTrain(rab)
confuMat(rab)
# illustrate regularized discriminant analysis
rda = MLearn(ALL.AML~., Golub_Merge[1:2000,], "rdacv", 1:35 )
confuMatTrain(rda)
confuMat(rda)

[Package MLInterfaces version 1.14.1 Index]