random.effects {mgcv} | R Documentation

## Random effects in GAMs

### Description

The smooth components of GAMs can be viewed as random effects for estimation purposes. This means that more conventional random effects terms can be incorporated into GAMs in two ways. The first method converts all the smooths into fixed and random components suitable for estimation by standard mixed modelling software. Once the GAM is in this form then conventional random effects are easily added, and the whole model is estimated as a general mixed model. `gamm` and `gamm4` from the `gamm4` package operate in this way.

The second method represents the conventional random effects in a GAM in the same way that the smooths are represented — as penalized regression terms. This method can be used with `gam` by making use of `s(...,"re")` terms in a model: see `smooth.construct.re.smooth.spec`. Alternatively, but less straightforwardly, the `paraPen` argument to `gam` can be used: see `gam.models`. If smoothing parameter estimation is by ML or REML (e.g. `gam(...,method="REML")`) then this approach is a completely conventional likelihood based treatment of random effects.

`gam` can be slow for fitting models with large numbers of random effects, because it does not exploit the sparcity that is often a feature of parametric random effects. It can not be used for models with more coefficients than data. However `gam` is often faster and more relaiable than `gamm` or `gamm4`, when the number of random effects is modest.

To facilitate the use of random effects with `gam`, `gam.vcomp` is a utility routine for converting smoothing parameters to variance components. It also provides confidence intervals, if smoothness estimation is by ML or REML.

Note that treating random effects as smooths does not remove the usual problems associated with testing variance components for equality to zero: see `summary.gam` and `anova.gam`.

### Author(s)

Simon Wood <simon.wood@r-project.org>

### References

Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36

Wood, S.N. (2008) Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society (B) 70(3):495-518

Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036

### See Also

`gam.vcomp`, `gam.models`, `smooth.terms`, `smooth.construct.re.smooth.spec`, `gamm`

### Examples

    ## see also examples for gam.models, gam.vcomp and gamm

    ## simple comparison of lme and gam

    require(nlme)
    b0 <- lme(travel~1,data=Rail,~1|Rail,method="REML")

    b <- gam(travel~s(Rail,bs="re"),data=Rail,method="REML")

    intervals(b0)
    gam.vcomp(b)

[Package mgcv version 1.7-18 Index]