Faster simulation using revdbayes

Performance comparisons

library(revdbayes)
# Is the microbenchmark package available?
got_microbenchmark <- requireNamespace("microbenchmark", quietly = TRUE)
if (got_microbenchmark) {
  library(microbenchmark)
}
# Set the number of posterior samples required.
n <- 1000
set.seed(46)

u <- quantile(gom, probs = 0.65)
fp <- set_prior(prior = "flat", model = "gp", min_xi = -1)
if (got_microbenchmark) {
  res <- microbenchmark(
    rpost = rpost(n = n, model = "gp", prior = fp, thresh = u, data = gom),
    rpost_rcpp = rpost_rcpp(n = n, model = "gp", prior = fp, thresh = u, 
                            data =   gom)
  )
  print(res, signif = 3)
  options(microbenchmark.unit = "relative")
  print(res, signif = 2)
}  
#> Unit: milliseconds
#>        expr  min   lq mean median   uq   max neval
#>       rpost 81.1 85.6 96.3   88.5 94.7 285.0   100
#>  rpost_rcpp 16.2 17.4 20.3   18.8 20.4  57.3   100
#> Unit: relative
#>        expr min  lq mean median  uq max neval
#>       rpost   5 4.9  4.7    4.7 4.7   5   100
#>  rpost_rcpp   1 1.0  1.0    1.0 1.0   1   100

mat <- diag(c(10000, 10000, 100))
pn <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)
if (got_microbenchmark) {
  res <- microbenchmark(
    rpost = rpost(n = n, model = "gev", prior = pn, data = portpirie),
    rpost_rcpp = rpost_rcpp(n = n, model = "gev", prior = pn, 
                            data = portpirie)
  )
}    
options(microbenchmark.unit = NULL)
print(res, signif = 3)
#> Unit: milliseconds
#>        expr   min    lq  mean median    uq max neval
#>       rpost 216.0 226.0 240.0  232.0 243.0 391   100
#>  rpost_rcpp  59.1  63.9  69.6   66.7  69.7 197   100
options(microbenchmark.unit = "relative")
print(res, signif = 2)
#> Unit: relative
#>        expr min  lq mean median  uq max neval
#>       rpost 3.6 3.5  3.5    3.5 3.5   2   100
#>  rpost_rcpp 1.0 1.0  1.0    1.0 1.0   1   100

# Informative prior set using revdbayes
pr2 <- set_prior(prob = 10^-(1:3), shape = c(38.9, 7.1, 47),
                 scale = c(1.5, 6.3, 2.6), model = "gev", prior = "quant")
if (got_microbenchmark) {
  res <- microbenchmark(
    rpost = rpost(n = n, model = "pp", prior = pr2, data = rainfall,
                  thresh = 40, noy = 54),
    rpost_rcpp = rpost_rcpp(n = n, model = "pp", prior = pr2, 
                            data = rainfall, thresh = 40, noy = 54)
  )
}
options(microbenchmark.unit = NULL)
print(res, signif = 3)
#> Unit: milliseconds
#>        expr   min    lq  mean median    uq max neval
#>       rpost 365.0 389.0 409.0  399.0 423.0 594   100
#>  rpost_rcpp  36.2  39.9  43.2   41.9  44.8  86   100
options(microbenchmark.unit = "relative")
print(res, signif = 2)
#> Unit: relative
#>        expr min  lq mean median  uq max neval
#>       rpost  10 9.8  9.5    9.5 9.4 6.9   100
#>  rpost_rcpp   1 1.0  1.0    1.0 1.0 1.0   100

Providing a user-defined prior

If the user wishes to supply their own prior to rpost_rcpp then they must first write a C++ function that evaluates the log of the prior density. The general way that rust (and hence revdbayes) enables users to provide their own C++ log-prior functions uses external pointers and is based on the Rcpp Gallery article Passing user-supplied C++ functions by Dirk Eddelbuettel.

The implementation in rust requires this C++ function to have a particular structure: it must take a constant reference to an Rcpp::NumericVector, say x, a constant reference to an Rcpp::List, say ppars, and return a double precision scalar. Here x is the argument of the prior density, i.e. the parameter vector of the extreme value model, and ppars is a list containing the values of prior parameters whose values are not specified inside the function. Thus values of any parameters in the prior can be changed without editing the function. If there are no such parameters then the argument ppars must still be present in the C++ function, even though the list provided to the function will be empty.

A simple way to provide C++ log-prior functions is to put them in a file, say user_fns.cpp, perhaps taking advantage of the R-like syntax made available by Rcpp sugar. Example content is provided below. This file is available on the revdbayes Github page.The functions in this file are compiled and made available to R, either using the Rcpp::sourceCpp function (e.g. Rcpp::sourceCpp("user_fns.cpp")) or using RStudio’s Source button on the editor toolbar. The example content below also includes the function create_prior_xptr, which creates an external pointer to a C++ function. See . It is this external pointer that is passed to set_prior to set the prior. If the user has written a C++ function, say new_name, they need to add to create_prior_xptr two lines of code:

else if (fstr == "new_name")  
  return(Rcpp::XPtr<funcPtr>(new funcPtr(&new_name))) ;

in order that they can create an external pointer for new_name using create_xptr.

The following excerpt from the example user_fns.cpp file contains a C++ function user_gp_flat to evaluate (the log of) a prior density \(\pi(\sigma, \xi) \propto \sigma^{-1}, \, \sigma > 0\), with an extra parameter min_xi enabling a prior lower bound to be set for \(\xi\). The same prior can be set, using an in-built prior function, using set_prior(prior = "flat", model = "gp", min_xi = -1), where we have set min_xi = -1. Note that . Hence in user_gp_flat \(\sigma\) and \(\xi\) are x[0] and x[1] not x[1] and x[2].

// [[Rcpp::depends(Rcpp)]]

#include <Rcpp.h>

using namespace Rcpp;

// [[Rcpp::interfaces(r, cpp)]]

// Generalized Pareto log-priors

// [[Rcpp::export]]
double user_gp_flat(const Rcpp::NumericVector& x, const Rcpp::List& ppars) {
  double min_xi = ppars["min_xi"] ;
  if (x[0] <= 0 || x[1] < min_xi)
    return R_NegInf ;
  return -log(x[0]) ;
}

// [[Rcpp::export]]
SEXP create_prior_xptr(std::string fstr) {
  typedef double (*priorPtr)(const Rcpp::NumericVector& x,
                  const Rcpp::List& ppars) ;
  if (fstr == "gp_flat") 
    return(Rcpp::XPtr<priorPtr>(new priorPtr(&user_gp_flat))) ;
  else
    return(Rcpp::XPtr<priorPtr>(R_NilValue)) ;
}

// We could create an external pointer when this file is sourced using
// this embedded R code below and/or (re)create them using the relevant
// pointer-creation functions in an R session or R package.

/*** R
  ptr_gp_flat <- create_prior_xptr("gp_flat")
*/

Once the external pointer to the user-supplied prior C++ function has been created it is passed to set_prior, along with any required parameter values. The following example repeats the example in Generalised Pareto (GP) model. The difference is that now we create the pointer ptr_gp_flat and pass it to set_prior using prior = ptr_gp_flat rather than using the arguments prior = "flat", model = "gp" to specify the equivalent in-built prior.

# GP model, user-defined prior
ptr_gp_flat <- create_prior_xptr("gp_flat")
p_user <- set_prior(prior = ptr_gp_flat, model = "gp", min_xi = -1)
gpg <- rpost_rcpp(n = 1000, model = "gp", prior = p_user, thresh = u,
                  data = gom)

References