tweedieGLMM {actuaRE} | R Documentation |
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
)
formula |
object of type |
data |
an object that is coercible by |
weights |
variable name of the exposure weight. |
muHatGLM |
indicates which estimate has to be used in the algorithm for the intercept term. Default is |
epsilon |
positive convergence tolerance |
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. |
an object of class cpglmm
, containing the model fit.
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
data("dataCar")
fitTweedieGLMM = tweedieGLMM(Y ~ area + gender + (1 | VehicleType / VehicleBody), dataCar,
weights = w, verbose = TRUE, epsilon = 1e-4)