kernlab (version 0.9-27)

predict.gausspr: predict method for Gaussian Processes object

Description

Prediction of test data using Gaussian Processes

Usage

# S4 method for gausspr
predict(object, newdata, type = "response", coupler = "minpair")

Arguments

object

an S4 object of class gausspr created by the gausspr function

newdata

a data frame or matrix containing new data

type

one of response, probabilities indicating the type of output: predicted values or matrix of class probabilities

coupler

Coupling method used in the multiclass case, can be one of minpair or pkpd (see reference for more details).

Value

response

predicted classes (the classes with majority vote) or the response value in regression.

probabilities

matrix of class probabilities (one column for each class and one row for each input).

References

• C. K. I. Williams and D. Barber Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998 http://www.dai.ed.ac.uk/homes/ckiw/postscript/pami_final.ps.gz

• T.F. Wu, C.J. Lin, R.C. Weng. Probability estimates for Multi-class Classification by Pairwise Coupling http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf

Examples

# NOT RUN {
## 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 <- gausspr(Class~.,data=genetrain,kernel="rbfdot",
                kpar=list(sigma=0.015))
gene

## predict gene type probabilities on the test set
genetype <- predict(gene,genetest,type="probabilities")
genetype
# }