ptglm: Poisson-Tweedie generalized linear model

In ptmixed: Poisson-Tweedie Generalized Linear Mixed Model

Poisson-Tweedie generalized linear model

Description

Estimates a Poisson-Tweedie generalized linear model.

Usage

ptglm(formula, offset = NULL, data, maxit = c(500, 1e+05), trace = T,
  theta.start = NULL)

Arguments

formula	A formula for the fixed effects part of the model. It should be in the form y ~ x1 + x2
offset	An offset to be added to the linear predictor. Default is NULL.
data	A data frame containing the variables declared in formula.
maxit	Vector containing the maximum number of iterations used in optim by the BFGS method and, if this fails, by the Nelder-Mead method
trace	Logical value. If TRUE, additional information is printed during the optimization. Default is TRUE.
theta.start	Numeric vector comprising initial parameter values for the vector of regression coefficients, the dispersion parameter and the power parameter (to be specified exactlyin this order!).

Value

A list containing the following elements: function's call (call); maximum likelihood estimate (mle); value of the loglikelihood at the mle (logl); convergence value (if 0, the optimization converged); the observed Fisher information (fisher.info) and the starting values used in the optimization (theta.init)

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 for the Poisson-Tweedie GLMM

Examples

data(df1, package = 'ptmixed')

# estimate the model
fit1 = ptglm(formula = y ~ group*time, data = df1)

# view model summary:
summary(fit1)

ptmixed documentation built on Aug. 18, 2022, 5:06 p.m.