varImpStruct-class {MLInterfaces} | R Documentation

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

### 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

    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")

[Package MLInterfaces version 1.22.0 Index]