# Tune parameters w and lamda using the CL penalty Does k-fold cross-validation with the function optimPenalLik and returns the values of lamda and w that maximize the area under the ROC. ```r tuneParam(Data, nfolds = nfolds, grid, algorithm = c("hjk", "QN")) ``` ## Arguments - `Data`: a data frame, as a first column should have the response variable y and the other columns the predictors - `nfolds`: the number of folds used for cross-validation. OBS! nfolds>=2 - `grid`: a grid (data frame) with values of lamda and w that will be used for tuning to tune the model. It is created by expand.grid see example below - `algorithm`: choose between BFGS ("QN") and hjk (Hooke-Jeeves optimization free) to be used for optmization ## Returns A matrix with the following: the average (over folds) cross-validated AUC, the totalVariables selected on the training set, and the standard deviation of the AUC over the nfolds. ## Details It supports the BFGS optimization method ('QN') from the optim stats function, the Hooke-Jeeves derivative-free minimization algorithm ('hjk') The value of lamda and w that yield the maximum AUC on the cross-validating data set is selected. ## Examples ```r ## Not run: set.seed(14) beta <- c(3, 2, -1.6, -4) noise <- 5 simData <- SimData(N=100, beta=beta, noise=noise, corr=TRUE) nfolds <- 3 grid <- expand.grid(w = c( 0.3, 0.7), lamda = c(1.5)) before <- Sys.time() paramCV <- tuneParam(simData, nfolds, grid, algorithm=c("QN")) (totalTime <- Sys.time() - before) maxAUC <- paramCV[which.max(paramCV$AUC),]$AUC allmaxAUC <- paramCV[which(paramCV$AUC==maxAUC),] # checks if the value of AUC # is unique; if is not unique then it will take the combination of lamda and # w where lamda has the largest value- thus achieving higher sparsity runQN <- optimPenaLik(simData, lamda= allmaxAUC[nrow(allmaxAUC),]$lamda, w= allmaxAUC[nrow(allmaxAUC),]$w, algorithms=c("QN")) (coefQN <- runQN$varQN) # check the robustness of the choice of lamda runQN2 <- optimPenaLik(simData, lamda= allmaxAUC[1,]$lamda, w= allmaxAUC[1,]$w, algorithms=c("QN")) (coefQN2 <- runQN2$varQN) ## End(Not run) ```

tuneParam function