Prediction error estimates

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Description

Extracts prediction error estimates from peperr objects.

Usage

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perr(peperrobject, 
    type = c("632p", "632", "apparent", "NoInf", "resample", "nullmodel"))

Arguments

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.

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.

Value

If type="632p" or type="632": Prediction error: Matrix, with one row per complexity value.

If 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.

If 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.

If type="resample": Matrix. Mean prediction error of resampling test samples, one row per complexity value.

If type="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.

References

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.

See Also

peperr, ipec

Examples

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## 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)

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