Class "varImpStruct" – collect data on variable importance from various machine learning methods

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Description

collects data on variable importance

Objects from the Class

Objects can be created by calls of the form new("varImpStruct", ...). These are matrices of importance measures with separate slots identifying algorithm generating the measures and variable names.

Slots

.Data:

Object of class "matrix" actual importance measures

method:

Object of class "character" tag

varnames:

Object of class "character" conformant vector of names of variables

Extends

Class "matrix", from data part. Class "structure", by class "matrix". Class "array", by class "matrix". Class "vector", by class "matrix", with explicit coerce. Class "vector", by class "matrix", with explicit coerce.

Methods

plot

signature(x = "varImpStruct"): make a bar plot, you can supply arguments plat and toktype which will use lookUp(...,plat,toktype) from the annotate package to translate probe names to, e.g., gene symbols.

show

signature(object = "varImpStruct"): simple abbreviated display

getVarImp

signature(object = "classifOutput", fixNames="logical"): extractor of variable importance structure; fixNames parameter is to remove leading X used to make variable names syntactic by randomForest (ca 1/2008). You can set fixNames to false if using hu6800 platform, because all featureNames are syntactic as given.

report

signature(object = "classifOutput", fixNames="logical"): extractor of variable importance data, with annotation; fixNames parameter is to remove leading X used to make variable names syntactic by randomForest (ca 1/2008). You can set fixNames to false if using hu6800 platform, because all featureNames are syntactic as given.

Examples

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library(golubEsets)
data(Golub_Merge)
library(hu6800.db)
smallG <- Golub_Merge[1001:1060,]
set.seed(1234)
opar=par(no.readonly=TRUE)
par(las=2, mar=c(10,11,5,5))
rf2 <- MLearn(ALL.AML~., smallG, randomForestI, 1:40, importance=TRUE,
 sampsize=table(smallG$ALL.AML[1:40]), mtry=sqrt(ncol(exprs(smallG))))
plot( getVarImp( rf2, FALSE ), n=10, plat="hu6800", toktype="SYMBOL")
par(opar)
report( getVarImp( rf2, FALSE ), n=10, plat="hu6800", toktype="SYMBOL")

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