tweedieGLMM {actuaRE} | R Documentation |

## 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 |

`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. |

### 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

### Examples

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

*actuaRE*version 0.1.5 Index]