twlss {mgcv} | R Documentation |

Tweedie family in which the mean, power and scale parameters can all depend on smooth linear predictors. Restricted to estimation via the extended Fellner Schall method of Wood and Fasiolo (2017). Only usable with `gam`

. Tweedie distributions are exponential family with variance given by *s*m^p* where *s* is a scale parameter, *p* a parameter (here between 1 and 2) and *m* is the mean.

twlss(link=list("log","identity","identity"),a=1.01,b=1.99)

`link` |
The link function list: currently no choise. |

`a` |
lower limit on the power parameter relating variance to mean. |

`b` |
upper limit on power parameter. |

A Tweedie random variable with 1<p<2 is a sum of `N`

gamma random variables
where `N`

has a Poisson distribution. The p=1 case is a generalization of a Poisson distribution and is a discrete
distribution supported on integer multiples of the scale parameter. For 1<p<2 the distribution is supported on the
positive reals with a point mass at zero. p=2 is a gamma distribution. As p gets very close to 1 the continuous
distribution begins to converge on the discretely supported limit at p=1, and is therefore highly multimodal.
See `ldTweedie`

for more on this behaviour.

The Tweedie density involves a normalizing constant with no closed form, so this is evaluated using the series
evaluation method of Dunn and Smyth (2005), with extensions to also compute the derivatives w.r.t. `p`

and the scale parameter.
Without restricting `p`

to (1,2) the calculation of Tweedie densities is more difficult, and there does not
currently seem to be an implementation which offers any benefit over `quasi`

. If you need this
case then the `tweedie`

package is the place to start.

An object inheriting from class `general.family`

.

Simon N. Wood simon.wood@r-project.org.

Dunn, P.K. and G.K. Smyth (2005) Series evaluation of Tweedie exponential dispersion model densities. Statistics and Computing 15:267-280

Tweedie, M. C. K. (1984). An index which distinguishes between some important exponential families. Statistics: Applications and New Directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference (Eds. J. K. Ghosh and J. Roy), pp. 579-604. Calcutta: Indian Statistical Institute.

Wood, S.N. and Fasiolo, M., (2017). A generalized Fellner-Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models. Biometrics, 73(4), pp.1071-1081. https://onlinelibrary.wiley.com/doi/full/10.1111/biom.12666

Wood, S.N., N. Pya and B. Saefken (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association 111, 1548-1575 http://dx.doi.org/10.1080/01621459.2016.1180986

library(mgcv) set.seed(3) n<-400 ## Simulate data... dat <- gamSim(1,n=n,dist="poisson",scale=.2) dat$y <- rTweedie(exp(dat$f),p=1.3,phi=.5) ## Tweedie response ## Fit a fixed p Tweedie, with wrong link ... b <- gam(list(y~s(x0)+s(x1)+s(x2)+s(x3),~1,~1),family=twlss(), data=dat) plot(b,pages=1) print(b) rm(dat)

[Package *mgcv* version 1.8-31 Index]