| knnB {MLInterfaces} | R Documentation |
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 ExpressionSet. 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.
knnB(exprObj, classifLab, trainInd, k = 1, l = 1, prob = TRUE, use.all = TRUE, metric = "euclidean")
exprObj |
An instance of the exprset class. |
classifLab |
The name of the phenotype variable to use for classification. |
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. |
See knn for a complete description of
parameters to and details of the k-nearest neighbor procedure
in the class package.
An object of class classifOutput-class.
Jess Mar, VJ Carey <stvjc@channing.harvard.edu>
# access and trim an ExpressionSet library(golubEsets) data(Golub_Merge) smallG <- Golub_Merge[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) stat.diag.daB( smallG, "ALL.AML", 1:40 )