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