createModel: Create the model with the optimal features

In LedPred: Learning from DNA to Predict Enhancers

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))

LedPred documentation built on Nov. 8, 2020, 8 p.m.