pg (version 0.2.4)

rpg_scalar: Sample from the Polya Gamma distribution PG(h, z)

Description

Chooses the most efficient implemented method to sample from a Polya Gamma distribution. Details on algorithm selection presented below.

Usage

rpg_scalar(h, z)
rpg_vector(n, h, z)
rpg_hybrid(h, z)
rpg_gamma(h, z, trunc = 1000L)
rpg_devroye(h, z)
rpg_sp(h, z)
rpg_normal(h, z)

Value

A single numeric value.

Arguments

h

integer values corresponding to the "shape" parameter.

z

numeric values corresponding to the "scale" parameter.

n

The number of samples to taken from a PG(h, z). Used only by the vector sampler.

trunc

Truncation cut-off. Only used by the gamma sampler.

Details

The following sampling cases are enabled:

• h > 170: Normal approximation method

• h > 13: Saddlepoint approximation method

• h = 1 or h = 2: Devroye method

• h > 0: Sum of Gammas method.

• h < 0: Result is automatically set to zero.

Examples

# Fixed parameter distribution simulation ----

## Parameters  ----
h = 1; z = .5

## Sample only one value  ----
single_value = rpg_scalar(h, z)
single_value

## Attempt distribution recovery  ----
vector_of_pg_samples = rpg_vector(1e6, h, z)

head(vector_of_pg_samples)
length(vector_of_pg_samples)

## Obtain the empirical results   ----
empirical_mean = mean(vector_of_pg_samples)
empirical_var = var(vector_of_pg_samples)

## Take the theoretical values ----
theoretical_mean = pg_mean(h, z)
theoretical_var = pg_var(h, z)

## Form a comparison table ----

# empirically sampled vs. theoretical values
rbind(c(empirical_mean, theoretical_mean),
      c(empirical_var, theoretical_var))

# Varying distribution parameters ----

## Generate varying parameters ----
u_h = 20:100
u_z = 0.5*u_h

## Sample from varying parameters ----
x = rpg_hybrid(u_h, u_z)