Tweedie {mgcv} | R Documentation |

Tweedie families, designed for use with `gam`

from the `mgcv`

library.
Restricted to variance function powers between 1 and 2. A useful alternative to `quasi`

when a
full likelihood is desirable. `Tweedie`

is for use with fixed `p`

. `tw`

is for use when `p`

is to be estimated during fitting. For fixed `p`

between 1 and 2 the Tweedie is an exponential family
distribution with variance given by the mean to the power `p`

.

`tw`

is only useable with `gam`

and `bam`

but not `gamm`

. `Tweedie`

works with all three.

Tweedie(p=1, link = power(0)) tw(theta = NULL, link = "log",a=1.01,b=1.99)

`p` |
the variance of an observation is proportional to its mean to the power |

`link` |
The link function: one of |

`theta` |
Related to the Tweedie power parameter by |

`a` |
lower limit on |

`b` |
upper limit on |

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.

`Tweedie`

is based partly on the `poisson`

family, and partly on `tweedie`

from the
`statmod`

package. It includes extra components to work with all `mgcv`

GAM fitting methods as well as
an `aic`

function.

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.

For `Tweedie`

, an object inheriting from class `family`

, with additional elements

`dvar` |
the function giving the first derivative of the variance function w.r.t. |

`d2var` |
the function giving the second derivative of the variance function w.r.t. |

`ls` |
A function returning a 3 element array: the saturated log likelihood followed by its first 2 derivatives w.r.t. the scale parameter. |

For `tw`

, an object of class `extended.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., 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(y~s(x0)+s(x1)+s(x2)+s(x3),family=Tweedie(1.25,power(.1)), data=dat) plot(b,pages=1) print(b) ## Same by approximate REML... b1 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=Tweedie(1.25,power(.1)), data=dat,method="REML") plot(b1,pages=1) print(b1) ## estimate p as part of fitting b2 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=tw(), data=dat,method="REML") plot(b2,pages=1) print(b2) rm(dat)

[Package *mgcv* version 1.8-31 Index]