penalBrier: Evaluation of the performance of risk prediction models with...
In stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Description Usage Arguments Details Value References Examples

Evaluation of the performance of risk prediction models with binary status response variable.

1	penalBrier(Data, coeffP)

`Data`	a data matrix; in the first column there should be the response variable y. If you give the training dataset it will calculate the Brier score.
`coeffP`	a named vector of coefficients

Brier score is a measure for classification performance of a binary classifier. Its values range between [0,1] and the closest is to 0 the better the classifier is. The area under the curve and the Brier score is used to summarize and compare the performance.

the Brier score (misclassification error)

Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review 78.

# use the penalBrier function on a simulated dataset, with given lamda and w.
## Not run: 
set.seed(14)
beta    <- c(3, 2, -1.6, -4)
noise   <- 5
simData <- SimData(N=100,beta=beta, noise=noise, corr=FALSE)

before   <- Sys.time()
stepPenal<- StepPenal(Data=simData, lamda=1.2, w=0.4)
(totRun  <- Sys.time() - before)

(coeff<- stepPenal$coeffP)
 me <- penalBrier(simData,coeff)

## End(Not run)