empb_gamma_poisson_c: empb_gamma_poisson_c

In alexandercoulter/DHBayes: Bayesian Hierarchical Sampler for Discrete (Binomial, Negative-Binomial, Poisson) Data

Description

empb_gamma_poisson_c

Usage

empb_gamma_poisson_c(
  df,
  eta = 1,
  tol = 1e-08,
  maxIter = 10000,
  starting_ab = NULL,
  method = c("newton", "gdescent")
)

Arguments

df	data.frame object, containing at least columns 'x' containing non-negative integer values, and 'g' containing group labels.
eta	positive numeric dampening parameter for Newton's method, gradient descent algorithm.
tol	non-negative numeric tolerance parameter for exiting optimization algorithm.
maxIter	positive integer setting maximum number of iterations for optimization algorithm.
starting_ab	optional 2-long numeric vector, giving initial algorithm starting point for fitting empirical Bayes estimates for a and b; default NULL.
method	string controlling optimization method; default 'newton'.

Value

list object containing empirical Bayes (EMPB) estimates of a, b hyperparameters, assuming df$x ~ poisson(L_g), and L_g ~ gamma(a, b), where 'L_g' denotes a group-level parameter.

Examples

# Generate example data:
set.seed(31)
a = 23
b = 9

# Number of groups:
NG = 10

# Creating group IDs:
g = replicate(NG, paste(sample(LETTERS, 10), sep="", collapse=""))

# Generating 'true' L parameters:
L = rgamma(length(g), a, b)

# Number of experiments, i.e. rows in df:
numexps = 100

# Filling df with pseudo data; note the requisite columns 'x' and 'g':
df = data.frame('x' = numeric(0), 'g' = character(0))
for(k in 1:numexps){
  gk = sample(g, 1)
  xk = rpois(1, L[g == gk])
  df = rbind(df, data.frame('x' = xk, 'g' = gk))
}

# Generating empirical Bayes (EMPB) solutions for a and b:
ab_fit = empb_gamma_poisson_c(df = df)

# Compare fitted values to known values:
cbind(c(a, b), c(ab_fit$a, ab_fit$b))

alexandercoulter/DHBayes documentation built on Dec. 19, 2021, 12:29 a.m.