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Eilers & Marx parameterization

Parameterization of the spline from Eilers & Marx (1996)

Model description

Since their introduction by Hastie & Tibshirani in the late 80ies, GAM's have become very popular. This example shows how to fit a GAM using penalized splines. The reason why GAM's can easily be handled in ADMB-RE is that penalized splines are a special case of random effects. ADMB-RE automatically estimates the degrees of freedom for each spline component, as this only amounts to estimate the variance of the random effects. A more detailed discussion of the model and the estimation approach can be found here: union.pdf


The data, which are available from Statlib (, contain information for each of 534 workers about whether they are members (y=1) of a workers union or not (y=0). The goal is to model the probability of membership as a function of various covariates.






See "Navigation" box to the left.

  • .tpl:  Model file
  • .dat: Data file
  • .pin: Starting values for the numerical optimizer
  • .par: Result file (what you get when you compile and run your model)