loglik.pt.1re | R Documentation |
Evaluates the loglikelihood of a Poisson-Tweedie generalized linear mixed model with random intercept, using the adaptive Gauss-Hermite quadrature rule.
loglik.pt.1re(beta, D, a, Sigma, y, X, Z, id, offset = NULL, GHk = 5, tol = 9.88131291682493e-324, GHs = NULL)
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 |
The loglikelihood value obtained using a Gauss-Hermite quadrature
approximation with GHk
quadrature points.
Mirko Signorelli
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
ptmixed
and the examples therein
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