tweedieGLMM function

Fitting a Tweedie GLMM, using the initial estimates of hierCredTweedie

This function first estimates the random effects model using Ohlsson's GLMC algorithm (Ohlsson, 2008) and then uses these estimates as initial estimates when fitting a Tweedie GLMM.

tweedieGLMM(
  formula,
  data,
  weights,
  muHatGLM = FALSE,
  epsilon = 1e-04,
  maxiter = 500,
  verbose = FALSE,
  balanceProperty = TRUE
)

Arguments

• formula: object of type formula that specifies which model should be fitted. Syntax is the same as for lmer and glmer. For example, Yijkt ~ x1 + x2 + (1 | Industry / Branch).
• data: an object that is coercible by as.data.table, containing the variables in the model.
• weights: variable name of the exposure weight.
• muHatGLM: indicates which estimate has to be used in the algorithm for the intercept term. Default is TRUE, which used the intercept as estimated by the GLM. If FALSE, the estimate of the hierarchical credibility model is used.
• epsilon: positive convergence tolerance $\epsilon$; the iterations converge when $||\theta[k] - \theta[k - 1]||^2[[2]]/||\theta[k - 1]||^2[[2]] < \epsilon$. Here, $\theta[k]$ is the parameter vector at the $k^{th}$ iteration.
• maxiter: maximum number of iterations.
• verbose: logical indicating if output should be produced during the algorithm.
• balanceProperty: logical indicating if the balance property should be satisfied.

Returns

an object of class cpglmm, containing the model fit.

Examples

data("dataCar")
fitTweedieGLMM = tweedieGLMM(Y ~ area + gender + (1 | VehicleType / VehicleBody), dataCar,
 weights = w, verbose = TRUE, epsilon = 1e-4)

References

Campo, B.D.C. and Antonio, Katrien (2023). Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio. Scandinavian Actuarial Journal, doi: 10.1080/03461238.2022.2161413

See Also

cpglmm and hierCredTweedie