# dynfrail_dist: Distribution parameters for dynfrail In dynfrail: Fitting Dynamic Frailty Models with the EM Algorithm

## Description

Distribution parameters for dynfrail

## Usage

 ```1 2``` ```dynfrail_dist(dist = "gamma", theta = 2, pvfm = -1/2, lambda = 0.1, n_ints = NULL, times = NULL) ```

## Arguments

 `dist` One of 'gamma', 'stable' or 'pvf'. `theta` Frailty distribution parameter. Must be >0. `pvfm` Only relevant if `dist = 'pvf'` is used. It determines which PVF distribution should be used. Must be larger than -1 and not equal to 0. `lambda` Frailty autocorrelation parameter. Must be >0. `n_ints` For piece-wise constant frailty, the number of intervals. With `n_ints = 0`, the classical shared frailty scenario is obtained. `times` A vector of time points which determine the piecewise-constant interval for the frailty. Overrides `n_ints`.

## Details

The `theta` and `lambda` arguments must be positive. In the case of gamma or PVF, `theta` is the inverse of the frailty variance, i.e. the larger the `theta` is, the closer the model is to a Cox model. When `dist = "pvf"` and `pvfm = -0.5`, the inverse Gaussian distribution is obtained. For the positive stable distribution, the γ parameter of the Laplace transform is θ / (1 + θ), with the alpha parameter fixed to 1.

## Value

An object of the type `dynfrail_dist`, which is mostly used to denote the supported frailty distributions in a consistent way.

`dynfrail, dynfrail_control`
 ```1 2 3 4 5``` ```dynfrail_dist() # Compound Poisson distribution: dynfrail_dist(dist = 'pvf', theta = 1.5, pvfm = 0.5) # Inverse Gaussian distribution: dynfrail_dist(dist = 'pvf') ```