SimDesign (version 2.18)

rejectionSampling: Rejection sampling (i.e., accept-reject method)

Description

This function supports the rejection sampling (i.e., accept-reject) approach to drawing values from seemingly difficult (probability) density functions by sampling values from more manageable proxy distributions.

Usage

rejectionSampling(
  n,
  df,
  dg,
  rg,
  M,
  method = "optimize",
  interval = NULL,
  logfuns = FALSE,
  maxM = 1e+05,
  parstart = rg(1L),
  ESRS_Mstart = 1.0001
)

Value

returns a vector or matrix of draws (corresponding to the output class from rg) from the desired df

Arguments

n

number of samples to draw

df

the desired (potentially un-normed) density function to draw independent samples from. Must be in the form of a function with a single input corresponding to the values sampled from rg. Function is assumed to be vectorized (if not, see Vectorize)

dg

the proxy (potentially un-normed) density function to draw samples from in lieu of drawing samples from df. The support for this density function should be the same as df (i.e., when df(x) > 0 then dg(x) > 0). Must be in the form of a function with a single input corresponding to the values sampled from rg. Function is assumed to be vectorized (if not, see Vectorize)

rg

the proxy random number generation function, associated with dg, used to draw proposal samples from. Must be in the form of a function with a single input corresponding to the number of values to draw, while the output can either be a vector or a matrix (if a matrix, each independent observation must be stored in a unique row). Function is assumed to be vectorized (if not, see Vectorize)

M

the upper-bound of the ratio of probability density functions to help minimize the number of discarded draws and define the corresponding rescaled proposal envelope. When missing, M is computed internally by finding a reasonable maximum of log(df(x)) - log(dg(x)), and this value is returned to the console. When both df and dg are true probability density functions (i.e., integrate to 1) the acceptance probability is equal to 1/M

method

when M is missing, the optimization of M is done either by finding the mode of the log-density values ("optimize") or by using the "Empirical Supremum Rejection Sampling" method ("ESRS")

interval

when M is missing, for univariate density function draws, the interval to search within via optimize. If not specified, a sample of 5000 values from the rg function definition will be collected, and the min/max will be obtained via this random sample

logfuns

logical; have the df and dg function been written so as to return log-densities instead of the original densities? The FALSE default assumes the original densities are returned (use TRUE when higher accuracy is required when generating each density definition)

maxM

logical; if when optimizing M the value is greater than this cut-off then stop; ampler would likelihood be too efficient, or optimization is failing

parstart

starting value vector for optimization of M in multidimensional distributions

ESRS_Mstart

starting M value for the ESRS algorithm

Author

Phil Chalmers

Details

The accept-reject algorithm is a flexible approach to obtaining i.i.d.'s from a difficult to sample from (probability) density function where either the transformation method fails or inverse transform method is difficult to manage. The algorithm does so by sampling from a more "well-behaved" proxy distribution (with identical support, up to some proportionality constant M that reshapes the proposal density to envelope the target density), and accepts the draws if they are likely within the target density. Hence, the closer the shape of dg(x) is to the desired df(x), the more likely the draws are to be accepted; otherwise, many iterations of the accept-reject algorithm may be required, which decreases the computational efficiency.

References

Caffo, B. S., Booth, J. G., and Davison, A. C. (2002). Empirical supremum rejection sampling. Biometrika, 89, 745--754.

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

Examples

if (FALSE) {

# Generate X ~ beta(a,b), where a and b are a = 2.7 and b = 6.3,
# and the support is Y ~ Unif(0,1)
dfn <- function(x) dbeta(x, shape1 = 2.7, shape2 = 6.3)
dgn <- function(x) dunif(x, min = 0, max = 1)
rgn <- function(n) runif(n, min = 0, max = 1)

# when df and dg both integrate to 1, acceptance probability = 1/M
M <- rejectionSampling(df=dfn, dg=dgn, rg=rgn)
M
dat <- rejectionSampling(10000, df=dfn, dg=dgn, rg=rgn, M=M)
hist(dat, 100)
hist(rbeta(10000, 2.7, 6.3), 100) # compare

# obtain empirical estimate of M via ESRS method
M <- rejectionSampling(1000, df=dfn, dg=dgn, rg=rgn, method='ESRS')
M

# generate using better support function (here, Y ~ beta(2,6)),
#   and use log setup in initial calls (more numerically accurate)
dfn <- function(x) dbeta(x, shape1 = 2.7, shape2 = 6.3, log = TRUE)
dgn <- function(x) dbeta(x, shape1 = 2, shape2 = 6, log = TRUE)
rgn <- function(n) rbeta(n, shape1 = 2, shape2 = 6)
M <- rejectionSampling(df=dfn, dg=dgn, rg=rgn, logfuns=TRUE) # better M
M

## Alternative estimation of M
## M <- rejectionSampling(10000, df=dfn, dg=dgn, rg=rgn, logfuns=TRUE,
##                        method='ESRS')
dat <- rejectionSampling(10000, df=dfn, dg=dgn, rg=rgn, M=M, logfuns=TRUE)
hist(dat, 100)

#------------------------------------------------------
# sample from wonky (and non-normalized) density function, like below
dfn <- function(x){
    ret <- numeric(length(x))
    ret[x <= .5] <- dnorm(x[x <= .5])
    ret[x > .5] <-  dnorm(x[x > .5]) + dchisq(x[x > .5], df = 2)
    ret
}
y <- seq(-5,5, length.out = 1000)
plot(y, dfn(y), type = 'l', main = "Function to sample from")

# choose dg/rg functions that have support within the range [-inf, inf]
rgn <- function(n) rnorm(n, sd=4)
dgn <- function(x) dnorm(x, sd=4)

## example M height from above graphic
##  (M selected using ESRS to help stochastically avoid local mins)
M <- rejectionSampling(10000, df=dfn, dg=dgn, rg=rgn, method='ESRS')
M
lines(y, dgn(y)*M, lty = 2)
dat <- rejectionSampling(10000, df=dfn, dg=dgn, rg=rgn, M=M)
hist(dat, 100, prob=TRUE)

# true density (normalized)
C <- integrate(dfn, -Inf, Inf)$value
ndfn <- function(x) dfn(x) / C
curve(ndfn, col='red', lwd=2, add=TRUE)


#-----------------------------------------------------
# multivariate distribution
dfn <- function(x) sum(log(c(dnorm(x[1]) + dchisq(x[1], df = 5),
                   dnorm(x[2], -1, 2))))
rgn <- function(n) c(rnorm(n, sd=3), rnorm(n, sd=3))
dgn <- function(x) sum(log(c(dnorm(x[1], sd=3), dnorm(x[1], sd=3))))

# M <- rejectionSampling(df=dfn, dg=dgn, rg=rgn, logfuns=TRUE)
dat <- rejectionSampling(5000, df=dfn, dg=dgn, rg=rgn, M=4.6, logfuns=TRUE)
hist(dat[,1], 30)
hist(dat[,2], 30)
plot(dat)


}