MLearn-methods {MLInterfaces} | R Documentation |
Initial version of more lightweight interface; please do help(MLearn) for the more recent schema-based approach.
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.
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)