rpg_scalar {pg} | R Documentation |
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)
Arguments
h |
|
z |
|
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
orh = 2
: Devroye method -
h > 0
: Sum of Gammas method. -
h < 0
: Result is automatically set to zero.
Value
A single numeric
value.
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)
[Package pg version 0.2.4 Index]