Distribution families in mgcv
As well as the standard families documented in
family (see also
glm) which can be used with functions
mgcv also supplies some extra families, most of which are currently only usable with
gam, although some can also be used with
bam. These are described here.
The following families are in the exponential family given the value of a single parameter. They are usable with all modelling functions.
TweedieAn exponential family distribution for which the variance of the response is given by the mean response to the power
pis in (1,2) and must be supplied. Alternatively, see
The following families are for regression type models dependent on a single linear predictor, and with a log likelihood
which is a sum of independent terms, each coprresponding to a single response observation. Usable with
gam, with smoothing parameter estimation by
"ML" (the latter does not integrate the unpenalized and parameteric effects out of the marginal likelihood optimized for the smoothing parameters). Also usable with
ocatfor ordered categorical data.
twfor Tweedie distributed data, when the power parameter relating the variance to the mean is to be estimated.
nbfor negative binomial data when the
thetaparameter is to be estimated.
betarfor proportions data on (0,1) when the binomial is not appropriate.
scatscaled t for heavy tailed data that would otherwise be modelled as Gaussian.
ziPfor zero inflated Poisson data, when the zero inflation rate depends simply on the Poisson mean.
The following families implement more general model classes. Usable only with
gam and only with REML smoothing parameter estimation.
cox.phthe Cox Proportional Hazards model for survival data.
gaulssa Gaussian location-scale model where the mean and the standard deviation are both modelled using smooth linear predictors.
gevlssa generalized extreme value (GEV) model where the location, scale and shape parameters are each modelled using a linear predictor.
ziplssa `two-stage' zero inflated Poisson model, in which 'potential-presence' is modelled with one linear predictor, and Poisson mean abundance given potential presence is modelled with a second linear predictor.
mvn: multivariate normal additive models.
multinom: multinomial logistic regression, for unordered categorical responses.
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