MLEpoisBCD {BCD} | R Documentation |
Maximum Likelihood Estimation for a Bivariate Poisson Distribution via Conditional Specification
Description
Estimates the parameters of a bivariate Poisson distribution via Conditional Specification using maximum likelihood.
Usage
MLEpoisBCD(data, initial_values = NULL)
Arguments
data |
data frame or matrix with two columns, representing paired observations of count variables |
initial_values |
optional named list with initial values for the parameters: |
Details
The model estimates parameters from a joint distribution for (X, Y)
with the form:
P(X = x, Y = y) = K(\lambda_1, \lambda_2, \lambda_3) \frac{\lambda_1^x \lambda_2^y \lambda_3^{xy}}{x! y!},
where x, y = 0, 1, 2, \ldots
, and K(\lambda_1, \lambda_2, \lambda_3)
is the normalizing constant.
Value
A list of class "MLEpoisBCD"
containing:
lambda1
estimated lambda1.
lambda2
estimated lambda2.
lambda3
estimated dependence parameter (must be in (0, 1]).
logLik
Maximum log-likelihood achieved.
AIC
Akaike Information Criterion.
BIC
Bayesian Information Criterion.
convergence
Convergence status from the optimizer (0 means successful).
See Also
Examples
# Simulate data
data <- rpoisBCD(n = 50, lambda1 = 3, lambda2 = 5, lambda3 = 1)
result <- MLEpoisBCD(data)
print(result)
data(eplSeasonGoals)
MLEpoisBCD(eplSeasonGoals[["1819"]])
data(lensfaults)
MLEpoisBCD(lensfaults)