tweedieGLMM {actuaRE}

Fitting a Tweedie GLMM, using the initial estimates of hierCredTweedie

Description

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.

Usage

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.

Value

an object of class cpglmm, containing the model fit.

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

Examples

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