clvarseloutput-class {CMA} | R Documentation |
Object returned by all classifiers that can peform variable selection or compute variable importance. These are:
rfCMA
,
compBoostCMA
,
LassoCMA
,
ElasticNetCMA
clvarseloutput
extend both the class
cloutuput
and varsel
, s. below.
learnind
:y
:yhat
:prob
:numeric
matrix
whose rows
equals the number of predicted observations (length of y
/yhat
)
and whose columns equal the number of different classes in the learning set.
Rows add up to one.
Entry j,k
of this matrix contains the probability for the j
-th
predicted observation to belong to class k
.
Can be a matrix of NA
s, if the classifier used does not
provide any probabilitiesmethod
:mode
:character
, one of "binary"
(if the number of classes in the learning set is two)
or multiclass
(if it is more than two).varsel
:numeric
vector of variable importance measures (for Random Forest) or
absolute values of regression coefficients (for the other three methods mentionned above)
(from which the majority will be zero).
Class "cloutput"
, directly.
Class "varseloutput"
, directly.
show(cloutput-object)
for brief informationftable(cloutput-object)
to obtain a confusion matrix/cross-tabulation
of y
vs. yhat
, s. ftable,cloutput-method
.plot(cloutput-object)
to generate a probability plot of the matrix
prob
described above, s. plot,cloutput-method
roc(cloutput-object)
to compute the empirical ROC curve and the
Area Under the Curve (AUC) based on the predicted probabilities, s.roc,cloutput-method
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
rfCMA
, compBoostCMA
, LassoCMA
, ElasticNetCMA