createModel {LedPred} | R Documentation

## Create the model with the optimal features

### Description

`createModel` function creates a SVM model from the training data set with the selected features.

### Usage

    createModel(data, cl = 1, kernel = "radial", cost = 1, gamma = 1,
      valid.times = 10, feature.ranking = NULL, feature.nb = NULL,
      file.prefix = NULL)

### Arguments

`data` | data.frame containing the training set
`cl` | integer indicating the column number corresponding to the response vector that classify positive and negative regions (default = 1)
`kernel` | SVM kernel, a character string: "linear" or "radial". (default = "radial")
`cost` | The SVM cost parameter for both linear and radial kernels. If NULL (default), the function `mcTune` is run.
`gamma` | The SVM gamma parameter for radial kernel. If radial kernel and NULL (default), the function `mcTune` is run.
`valid.times` | Integer indicating how many times the training set will be split for the cross validation step (default = 10). This number must be smaller than positive and negative sets sizes.
`feature.ranking` | List of ordered features.
`feature.nb` | the optimal number of feature to use from the list of ordered features.
`file.prefix` | A character string that will be used as a prefix followed by "_model.RData" for the resulting model file, if it is NULL (default), no model is saved

### Value

the best SVM model

### Examples

        data(crm.features)
        cost <- 1
        gamma <- 1
        data(feature.ranking)
        feature.nb <- 70
    #svm.model <- createModel(data.granges=crm.features, cost=cost, gamma=gamma,
    #    feature.ranking=feature.ranking, feature.nb=feature.nb)
    #feature.weights <- as.data.frame(t(t(svm.model$coefs) %*% svm.model$SV))

[Package LedPred version 1.16.0 Index]