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)
The 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 revdbayes
are set_prior
and rpost
. 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 syntax of rpost
and post_rcpp
is identical. For example:
gevp_rcpp <- rpost_rcpp(n = 1000, model = "gev", prior = pn, data = portpirie)
To get the current released version from CRAN:
install.packages("revdbayes")
See 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")
The arguments to d/p/q/rgev
and d/p/q/rgp
now obey the usual conventions for R's dpqr probability distribution functions.
In pp_check.evpost
the argument subtype
is now documented properly.
The 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 predict.evpost
with 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 stats::nlminb
.
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
and rpost_rcpp
meant that it was not possible to supply the argument r
to be passed to rust::ru
or 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 rpost
using 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 function: rpost_rcpp
.
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.prob
and 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 dgev
, pgev
, qgev
, rgev
and the GP functions dgp
, pgp
, qgp
, 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
is 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 points
.
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 evdbayes
package.
Unnecessary dependence on package devtools
via Suggests is removed.
Bugs fixed in the (R) prior functions gp_norm
, gev_norm
and gev_loglognorm
. The effect of the bug was negligible unless the prior variances are not chosen to be large.
In a call to rpost
or rpost_rcpp
with 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.
S3 predict()
method for class 'evpost' performs predictive inference about the largest observation observed in N years, returning an object of class evpred
.
S3 plot()
for the evpred
object returned by predict.evpost
.
S3 pp_check()
method for class 'evpost' performs posterior predictive checks using the bayesplot package.
Interface to the bayesplot package added in the S3 plot.evpost
method.
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 a
everywhere.
In set_prior
with prior = "beta"
parameter vector ab
has been corrected to pq
.
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.