caretNuSVRModelInfo.R

    modelInfo <- list(label = "nu-SVR with Radial Basis Function Kernel",
                      library = "e1071",
                      type = c("Regression"),
                      parameters = data.frame(parameter = c("nu", "C", "gamma"),
                                              class = c("numeric", "numeric", "numeric"),
                                              label = c("Nu", "Cost", "Gamma")),
                      loop = NULL,
                      grid=NULL,
                      fit = function(x, y, wts, param, lev, last, classProbs, ...) { 
                        if(any(names(list(...)) == "prob.model") | is.numeric(y)) {
                          out <- svm(x = as.matrix(x), y = y,
                                     scale=FALSE,
                                     type='nu-regression',
                                     kernel = 'radial',
                                     gamma = param$gamma,
                                     cost = param$C,
                                     nu = param$nu, ...)
                        }
                        out            
                      },
                      predict = function(modelFit, newdata, submodels = NULL) {
                        predict(modelFit, newdata=newdata)
                      },
                      prob = function(modelFit, newdata, submodels = NULL) {
                        FALSE
                      },
                      predictors = function(x, ...){
                        FALSE
                      },
                      tags = c("Kernel Method", "Support Vector Machines", "Radial Basis Function",
                               "Robust Methods"),
                      levels = function(x) lev(x),
                      sort = function(x) {
                        # If the cost is high, the decision boundary will work hard to
                        # adapt. Also, if C is fixed, smaller values of sigma yeild more
                        # complex boundaries
                        x[order(x$C, -x$gamma),]
                      })