> Gad Abraham wrote: >> Hi, >> In the boot package, the original statistic is simply the statistic function evaluated on the original data (called t0). >> However, in Harrell et al 1996 "Multivariable prognostic models..." Stats Med vol 15, pp. 361--387, it is different (p. 372): >> The statistic function evaluated on the original data is called "D_app" (apparent statistic), whereas "D_orig" (original statistic) is derived by first fitting the model to a bootstrap sample, then freezing the model and applying it to the original data. The >> bootstrap statistic D_boot is derived by applying the function to the bootstrap sample. Then optimism O = average(D_boot - D_orig), and the bias-corrected estimate is D_app - O. >> Can someone explain this difference, and especially why boot >> doesn't evaluate the frozen model again on the original data? >> Thanks, >> Gad > > The optimism bootstrap is a different kind of bootstrap to estimate bias from overfitting. boot is for the regular bootstrap.
Thanks. Is there a general-purpose optimism bootstrap function in CRAN? It seems that Design:::validate does the optimism bootstrap but only for cases like lrm and cph; I'd like to validate the mean-square error of a time series method like arima or forecast:::ets. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
