predict.ksvm {kernlab} | R Documentation |
Prediction of test data using support vector machines
## S4 method for signature 'ksvm': predict(object, newdata, type = "response", coupler = "minpair")
object |
an S4 object of class ksvm created by the
ksvm function |
newdata |
a data frame or matrix containing new data |
type |
one of response , probabilities
,votes , decision
indicating the type of output: predicted values, matrix of class
probabilities, matrix of vote counts, or matrix of decision values. |
coupler |
Coupling method used in the multiclass case, can be one
of minpair or pkpd (see reference for more details). |
If type(object)
is C-svc
,
nu-svc
, C-bsvm
or spoc-svc
the vector returned depends on the argument type
:
response |
predicted classes (the classes with majority vote). |
probabilities |
matrix of class probabilities (one column for each class and one row for each input). |
votes |
matrix of vote counts (one column for each class and one row for each new input) |
If type(object)
is eps-svr
, eps-bsvr
or
nu-svr
a vector of predicted values is returned.
If type(object)
is one-classification
a vector of
logical values is returned.
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
## example using the promotergene data set data(promotergene) ## create test and training set ind <- sample(1:dim(promotergene)[1],20) genetrain <- promotergene[-ind, ] genetest <- promotergene[ind, ] ## train a support vector machine gene <- ksvm(Class~.,data=genetrain,kernel="rbfdot",kpar=list(sigma=0.015),C=70,cross=4,prob.model=TRUE) gene ## predict gene type probabilities on the test set genetype <- predict(gene,genetest,type="probabilities") genetype