Provides functions for the Bayesian analysis of extreme value
models. The 'rust' package < https://cran.r-project.org/package=rust> is
used to simulate a random sample from the required posterior distribution.
The functionality of 'revdbayes' is similar to the 'evdbayes' package
< https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
Monte Carlo ('MCMC') methods for posterior simulation. Also provided
are functions for making inferences about the extremal index, using
the K-gaps model of Suveges and Davison (2010)
revdbayes package uses the ratio-of-uniforms method to produce random samples from the posterior distributions that occur in some relatively simple Bayesian extreme value analyses. The functionality of revdbayes is similar to the
evdbayes package, which uses Markov Chain Monte Carlo (MCMC) methods for posterior simulation. Advantages of the ratio-of-uniforms method over MCMC in this context are that the user is not required to set tuning parameters nor to monitor convergence and a random posterior sample is produced. Use of the Rcpp package enables
revdbayes to be faster than
evdbayes. Also provided are functions for making inferences about the extremal index, using the K-gaps model of Suveges and Davison (2010).
The two main functions in
set_prior sets a prior for extreme value parameters.
rpost samples from the posterior produced by updating this prior using the likelihood of observed data under an extreme value model. The following code sets a prior for Generalised Extreme Value (GEV) parameters based on a multivariate normal distribution and then simulates a random sample of size 1000 from the posterior distribution based on a dataset of annual maximum sea levels.
data(portpirie)mat <- diag(c(10000, 10000, 100))pn <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)gevp <- rpost(n = 1000, model = "gev", prior = pn, data = portpirie)plot(gevp)
From version 1.2.0 onwards the faster function
rpost_rcpp can be used.
See the vignette "Faster simulation using revdbayes and Rcpp" for details. The functions
post_rcpp have the same syntax. For example:
gevp_rcpp <- rpost_rcpp(n = 1000, model = "gev", prior = pn, data = portpirie)
To get the current released version from CRAN:
vignette("revdbayes-vignette", package = "revdbayes") for an overview of the package and
vignette("revdbayes-using-rcpp-vignette", package = "revdbayes") for an illustration of the improvements in efficiency produced using the Rcpp package. See
vignette("revdbayes-predictive-vignette", package = "revdbayes") for an outline of how to use revdbayes to perform posterior predictive extreme value inference. Inference for the extremal index using the K-gaps model is described in
vignette("revdbayes-kgaps-vignette", package = "revdbayes")
LF line endings used in inst/include/revdbayes.h and inst/include/revdbayes_RcppExports.h to avoid CRAN NOTE.
The format of the
data supplied to
rpost_rcpp() is checked and an error is thrown if it is not appropriate.
rpost_rcpp() an error is thrown if the input threshold
thresh is lower than the smallest observation in
data. This is only relevant when
model = "gp",
model = "bingp" or
model = "pp".
The summary method for class "evpost" is now set up according to Section 8.1 of the R FAQ at (https://cran.r-project.org/doc/FAQ/R-FAQ.html).
A bug in
grimshaw_gp_mle has been fixed, so that now solutions with K greater than 1 are discarded. (Many thanks to Leo Belzile.)
grimshaw_gp_mle using the starting value equal to the upper bound can result in early termination of the Newton-Raphson search. A starting value away from the upper bound is now used (lines 282 and 519 of frequentist.R). (Many thanks to Jeremy Rohmer for sending me a dataset that triggered this problem.)
prior = "norm" or
prior = "loglognorm" then an explicit error is thrown if
cov is not supplied. (Many thanks to Leo Belzile.)
The mathematics in the reference manual has been tidied.
The arguments to
d/p/q/rgp now obey the usual conventions for R's dpqr probability distribution functions.
pp_check.evpost the argument
subtype is now documented properly.
conf argument to
kgaps_mle didn't work properly:
conf = 95 was always used. This has been corrected.
Bayesian and maximum likelihood inference for the K-gaps model for inferring the extremal index using threshold inter-exceedances times. [Suveges, M. and Davison, A. C. (2010), Model misspecification in peaks over threshold analysis, The Annals of Applied Statistics, 4(1), 203-221. doi:10.1214/09-AOAS292.]
New vignette: "Inference for the extremal index using the K-gaps model".
Added the attribute
attr(gom, "npy") (with value 3) to the
gom dataset. This is for compatability with the threshr package.
Give an explicit error message if
plot.evpost is called with the logically incompatible arguments
add_pu = TRUE and
pu_only = TRUE.
The documentation for
set_bin_prior has been corrected: only in-built priors are available, i.e. it is not possible for the user to supply their own prior.
In some extreme cases (datasets with very small numbers of threshold excesses) calling
type = "q" and
x close to 1 returns an imprecise value for the requested predictive quantiles. This has been corrected by using
stats::uniroot rather than
A bug (missing
drop = FALSE in subsetting a matrix) in
plot.evpred produced an error message if
n_years was scalar in the prior call to
predict.evpost. This bug has been corrected.
The placing of ... in the function definitions of
rpost_rcpp meant that it was not possible to supply the argument
r to be passed to
rust::ru_rcpp to change the ratio-of-uniforms tuning parameter
r. Furthermore, if
model = "os" then trying to do this sets
ros in error. This has been corrected.
A bug meant that the values returned by
predict(evpost_object, type = "d") being incorrect if
evpost_object was returned from a call to
model = bingp. The values returned were too small: they differ from the correct values by a factor approximately equal to the proportion of observations that lie above the threshold. This bug has been corrected.
Faster computation, owing to the use of packages Rcpp and RcppArmadillo in package rust (https://CRAN.R-project.org/package=rust).
New vignette. "Faster simulation using revdbayes".
set_prior has been extended so that informative priors for GEV parameters can be specified using the arguments
prior = "prob" or
prior = "quant". It is no longer necessary to use the functions
prior.quant from the evdbayes package to set these priors.
The list returned from
set_prior now contains default values for all the required arguments of a given in-built prior, if these haven't been specified by the user. This simplifies the evaluation of prior densities using C++.
The GEV functions
rgev and the GP functions
rgp have been rewritten to conform with the vectorised style of the standard functions for distributions, e.g. those found at
?Normal. This makes these functions more flexible, but also means that the user take care when calling them with vectors arguments or different lengths.
The documentation for
rpost has been corrected: previously it stated that the default for
use_noy = FALSE, when in fact it is
use_noy = TRUE.
Bug fixed in
plot.evpost : previously, in the
d = 2 case, providing the graphical parameter
col produced an error because
col = 8 was hard-coded in a call to
points. Now the extra argument
points_par enables the user to provide a list of arguments to
All the (R, not C++) prior functions described in the documentation of
set_prior are now exported. This means that they can now be used in the function
posterior in the
Unnecessary dependence on package
devtools via Suggests is removed.
Bugs fixed in the (R) prior functions
gev_loglognorm. The effect of the bug was negligible unless the prior variances are not chosen to be large.
In a call to
model = "os" the user may provide
data in the form of a vector of block maxima. In this instance the output is equivalent to a call to these functions with
model = "gev" with the same data.
A new vignette (Posterior Predictive Extreme Value Inference using the revdbayes Package) provides an overview of most of the new features. Run browseVignettes("revdbayes") to access.
predict() method for class 'evpost' performs predictive inference about the largest observation observed in N years, returning an object of class
plot() for the
evpred object returned by
pp_check() method for class 'evpost' performs posterior predictive checks using the bayesplot package.
Interface to the bayesplot package added in the S3
model = bingp can now be supplied to
rpost() to add inferences about the probability of threshold exceedance to inferences about threshold excesses based on the Generalised Pareto (GP) model.
set_bin_prior() can be used to set a prior for this probability.
rprior_quant(): to simulate from the prior distribution for GEV parameters proposed in Coles and Tawn (1996) [A Bayesian analysis of extreme rainfall data. Appl. Statist., 45, 463-478], based on independent gamma priors for differences between quantiles.
prior_prob(): to simulate from the prior distribution for GEV parameters based on Crowder (1992), in which independent beta priors are specified for ratios of probabilities (which is equivalent to a Dirichlet prior on differences between these probabilities).
The spurious warning messages relating to checking that the model argument to
rpost() is consistent with the prior set using
set-prior() have been corrected. These occurred when
model = "pp" or
model = "os".
The hyperparameter in the MDI prior was
a in the documentation and
a_mdi in the code. Now it is
prior = "beta" parameter vector
ab has been corrected to
In the documentation of
rpost() the description of the argument
noy has been corrected.
Package spatstat removed from the Imports field in description to avoid NOTE in CRAN checks.