varImpStruct-class {MLInterfaces} | R Documentation |
collects data on variable importance
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.
.Data
:"matrix"
actual importance
measures method
:"character"
tag varnames
:"character"
conformant
vector of names of variables
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.
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.signature(object = "varImpStruct")
: simple abbreviated
display 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.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.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")