bootgpd(x, R = 100, trace = 10)gpdR = 99.trace replicates. Defaults to trace = 10.The print method returns the original point estimates, bootstrap means, bootstrap estimates of bias and standard deviation. The bootstrap median is also returned.
Efron and Tibshirani state that ``As a rule of thumb, a bias of less than 0.25 standard errors can be ignored, unless we are trying to do careful confidence interval calculations'' (Chapter 10, page 128). As such, the functions warn the user if any ratio of estimated bias to standard error exceeds this level.
The summary method returns the same as the print method, but also the
bootstrap estimate of the covariance of the parameters. When printed,
the correlation (not covariance) is displayed. The covariance might
be wanted to pass into gpd using method = "simulate". In
particular, in some circumstances the numerical estimate of the Hessian
of the parameters can be unstable, resulting in the Metropolis algorithm
using a proposal distribution that is too narrow. This shows up as the
acceptance rate of the algorithm being too high (above about 45%).
Then, using a bootstrap estimate might be preferable.
The plot method displays histograms and kernel density estimates.
gpdmod <- gpd(log(ALT.M), data=liver, qu=.7, xi=~as.numeric(dose))
bmod <- bootgpd(mod)
summary(bmod)
par(mfrow=c(1,3))
plot(bmod)Run the code above in your browser using DataLab