loglik.pt.1re {ptmixed}

Loglikelihood of Poisson-Tweedie generalized linear mixed model with random intercept

Description

Evaluates the loglikelihood of a Poisson-Tweedie generalized linear mixed model with random intercept, using the adaptive Gauss-Hermite quadrature rule.

Usage

loglik.pt.1re(beta, D, a, Sigma, y, X, Z, id, offset = NULL, GHk = 5,
  tol = 9.88131291682493e-324, GHs = NULL)

Arguments

beta

Vector of regression coefficients

D

Dispersion parameter (must be > 1)

a

Power parameter (must be < 1)

Sigma

A matrix with the variance of the random intercept

y

Response vector (discrete)

X

Design matrix for the fixed effects

Z

Design matrix for the random effects

id

Id indicator (it should be numeric)

offset

Offset term to be added to the linear predictor

GHk

Number of quadrature points (default is 5)

tol

Tolerance value for the evaluation of the probability mass function of the Poisson-Tweedie distribution

GHs

Quadrature points at which to evaluate the loglikelihood. If NULL (default value), the GH quadrature points are computed internally

Value

The loglikelihood value obtained using a Gauss-Hermite quadrature approximation with GHk quadrature points.

Author(s)

Mirko Signorelli

References

Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, 21 (6), 520-545. URL: https://doi.org/10.1177/1471082X20936017

See Also

ptmixed and the examples therein