KBModelAccessors: KBModel Accessors

Description Usage Arguments Value Accessor-like methods Author(s) References Examples

Description

KBModel Accessors

Usage

1
2
3
4
## S4 method for signature 'KBModel'
modelOffset(object)

getSVMSlotValue(paramName, model, raw = FALSE)

Arguments

object

a KeBABS model

paramName

unified name of an SVM model data element

model

a KeBABS model

raw

when set to TRUE the parameter value is delivered in exactly the way as it is stored in the SVM specific model, when set to FALSE it is delivered in unified format

Value

getSVMSlotValue: value of requested parameter in unified or native format dependent on parameter raw.

Accessor-like methods

modelOffset: returns the model offset.

featureWeights: returns the feature weights.

SVindex: returns the support vector indices for the training samples.

cvResult: returns result of cross validation as object of class CrossValidationResult.

modelSelResult: returns result of model selection as object of class ModelSelectionResult.

svmModel: returns the native svm model stored within KeBABS model.

probabilityModel: returns the probability model stored within KeBABS model.

Author(s)

Johannes Palme <[email protected]>

References

http://www.bioinf.jku.at/software/kebabs

J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574-2576, 2015. DOI: 10.1093/bioinformatics/btv176.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
## create kernel object for normalized spectrum kernel
specK5 <- spectrumKernel(k=5)
## Not run: 
## load data
data(TFBS)

## perform training - feature weights are computed by default
model <- kbsvm(enhancerFB, yFB, specK5, pkg="LiblineaR",
               svm="C-svc", cost=15, cross=10, showProgress=TRUE)
               showProgress=TRUE)

## show result of validation
cvResult(model)
## show feature weights
featureWeights(model)[1:5]
## show model offset
modelOffset(model)

## End(Not run)

kebabs documentation built on Nov. 17, 2017, 1:20 p.m.