tuneParam: Tune parameters w and lamda using the CL penalty

Description Usage Arguments Details Value Examples

View source: R/add.r

Description

Does k-fold cross-validation with the function optimPenalLik and returns the values of lamda and w that maximize the area under the ROC.

Usage

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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

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.

Value

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.

Examples

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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)

stepPenal documentation built on May 29, 2017, 3:25 p.m.