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Generate a random sample from a MOBSTER model.

Source: R/density.R
rdbpmm.Rd

This function can be used to generate data from a MOBSTER model, or from a custom mixture. The principle is the same of function ddbpmm.

Usage

rdbpmm(
  x,
  a = NULL,
  b = NULL,
  pi = NULL,
  shape = NULL,
  scale = NULL,
  n = 1,
  tail.cutoff = 1
)

Arguments

x

An object of class "dbpmm".

a

Vector of parameters for the Beta components; if this is null values stored inside x are used.

b

Vector of parameters for the Beta components; if this is null values stored inside x are used.

pi

Mixing proportions for the mixture; if this is null values stored inside x are used.

shape

Shape of the power law; if this is null values stored inside x are used.

scale

Scale of the power law; if this is null values stored inside x are used.

n

Number of samples required (i.e., points).

tail.cutoff

Because the Pareto Type I power law has support over all the positive real line, tail values above tail.cutoff are rejected (and re-sampled).

Value

n samples from the mixture

Examples

library(ggplot2)
# 1 Beta component at 0.5 mean (symmetrical Beta)
a = b = 50
names(a) = names(b) = "C1"

# 60% tail mutations
pi = c('Tail' = .6, 'C1' = .4)

# Sample
v = data.frame(x = rdbpmm(
   x = NULL,
   n = 1000,
   a = a,
   b = b,
   pi = pi,
   shape = 2,
   scale = 0.05
 ))
ggplot(v, aes(x)) + geom_histogram(binwidth = 0.01)


# Or use the parameters of a model available
data('fit_example', package = 'mobster')

v = data.frame(x = rdbpmm(x = fit_example$best, n = 1000))
ggplot(v, aes(x)) + geom_histogram(binwidth = 0.01)