perr {peperr} | R Documentation |
Extracts prediction error estimates from peperr
objects.
perr(peperrobject, type = c("632p", "632", "apparent", "NoInf", "resample", "nullmodel"))
peperrobject |
peperr object obtained by call to function peperr . |
type |
"632p" for the .632+ prediction error estimate (default), "632" for the .632 prediction error estimate. "apparent" , "NoInf" , "resample" and "nullmodel" return the apparent error, the no-information error, the mean sample error and the nullmodel fit, see Details. |
The .632 and the .632+ prediction error estimates are weighted combinations of the apparent error and bootstrap cross-validation error estimate, for survival data at given time points.
type="632p"
and type="632"
: Prediction error: Matrix, with one row per complexity value.
type="apparent"
: Apparent error of the full data set. Matrix: One row per complexity value. In case of survival response, columns correspond to evaluation timepoints, which are given in attribute addattr
.
type="NoInf"
: No information error of the full data set, i. e. evaluation in permuted data. Matrix: One row per complexity value. Columns correspond to evaluation timepoints, which are given in attribute addattr
.
"resample"
: Matrix. Mean prediction error of resampling test samples, one row per complexity value.
"nullmodel"
{Vector or scalar: Null model prediction error, i.e. of fit without information of covariates. In case of survival response Kaplan-Meier estimate at each time point, if response is binary logistic regression model, else not available.}
Binder, H. and Schumacher, M. (2008) Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Statistical Applications in Genetics and Molecular Biology, 7:1.
Gerds, T. and Schumacher, M. (2007) Efron-type measures of prediction error for survival analysis. Biometrics, 63, 1283–1287.
Schumacher, M. and Binder, H., and Gerds, T. (2007) Assessment of Survival Prediction Models in High-Dimensional Settings. Bioinformatics, 23, 1768-1774.
## Not run: n <- 200 p <- 100 beta <- c(rep(1,10),rep(0,p-10)) x <- matrix(rnorm(n*p),n,p) real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta))) cens.time <- rexp(n,rate=1/10) status <- ifelse(real.time <= cens.time,1,0) time <- ifelse(real.time <= cens.time,real.time,cens.time) # Example: # Obtain prediction error estimate fitting a Cox proportional hazards model # using CoxBoost # through 10 bootstrap samples # with fixed complexity 50 and 75 # and aggregate using prediction error curves peperr.object <- peperr(response=Surv(time, status), x=x, fit.fun=fit.CoxBoost, complexity=c(50, 75), indices=resample.indices(n=length(time), method="sub632", sample.n=10)) # 632+ estimate for both complexity values at each time point perr(peperr.object) ## End(Not run)