How are random effects used in the mgcv?

random.effects: Random effects in GAMs In mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. 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.

How to choose the number of knots in mgcv?

This post (link below) alludes to setting the basis dimension to -1 (k = -1) as automatically choosing the number of knots via Generalized Cross Validation (GCV) in R’s mgcv package: except that k is NOT the number of knots. I’ve only come across this specific instance of setting k = -1.

How is the fix argument used in mgcviz?

Here is an example where we are plotting a 2D slice of a 3D smooth effect, with confidence surfaces: The fix argument is used to determine where the 3D effect should be sliced along the z-axis. The plot also shows a subset of residuals (colour-coded depending on sign) that fall close (in term of Euclidean distance) to the selected slice.

Are there any model checks provided by mgcv?

Most of the model checks provided by mgcv are contained in qq.gam and gam.check. mgcViz substitutes them with the more advanced qq.gamViz and check.gamViz methods. Consider the following model with binomial responses:

What kind of smooths are used in mgcv?

The sorts of smooths we fit in mgcv are (typically) penalized smooths; we choose to use some number of basis functions k, which sets an upper limit on the complexity — wiggliness — of the smooth, and then we estimate parameters for the model by maximizing a penalized log-likelihood.

Is there an exclude argument in mgcv predict?

Thank you! From version 1.8.8 of mgcv predict.gam has gained an exclude argument which allows for the zeroing out of terms in the model, including random effects, when predicting, without the dummy trick that was suggested previously.

How are log likelihood and penalty related in mgcv?

The log-likelihood of the model is a measure of the fit (or lack there of), while the penalty helps us avoid fitting overly complex smooths. In the sorts of models that can be fitted in mgcv, the penalty is a function of the model coefficients, β, and a penalty matrix 1, which we write as S. The penalty then is β T S β.

How are random effects represented in mixed GAM?

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.

Can a Gams be used as a random effect?

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.

Which is the best method for smoothing parameter estimation?

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 sparsity that is often a feature of parametric random effects.

Is it possible to use random effects in GAM?

Random effects implemented in this way do not exploit the sparse structure of many random effects, and may therefore be relatively inefficient for models with large numbers of random effects, when gamm4 or gamm may be better alternatives. Note also that gam will not support models with more coefficients than data.

Which is more reliable, Gam or gamm4?

However gam is often faster and more reliable 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.

How are random effects used in the mgcv?