importance {randomForest} | R Documentation |
This is the extractor function for variable importance measures as
produced by randomForest
.
## S3 method for class 'randomForest': importance(x, type=NULL, class=NULL, scale=TRUE, ...)
x |
an object of class randomForest |
type |
either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy, 2=mean decrease in node impurity). |
class |
for classification problem, which class-specific measure to return. |
scale |
For permutation based measures, should the measures be divided their ``standard errors''? |
... |
not used. |
See the documentation for randomForest
for explanation
of how the importance measures are computed.
If class
and type
are NULL
, A matrix of
p
rows and nclass + 2
columns (where p
is the
number of variables in the data and nclass
is the number of
classes) for classification problem, or p
rows and 2
columns for regression. In the classification case, the first
nclass
columns are the class-specific importance measures
(based on permutation of out-of-bag data). The nclass+1
st
column is the overall importance, and the last column is the overall
measure based on the decrease in node purity (or `Gini-based'
measure).
If either type
or class
is given, it returns a
(named) vector of importance measure, one for each predictor variable.