The figures don't obviously scream out `overfitting' to me, and the standard
errors don't look excessively wide, given the data. Unless there is a strong
reason for using `lo', you could also try the `gam' function in package
`mgcv': it attempts to estimate the appropriate degree of smoothing
a
thomas L Jones asks:
> The subject is a Generalized Additive Model. Experts caution us
> against overfitting the data, which can cause inaccurate results.
Inaccurate *predictions*, to be more precies. The main problem with
overfitting is that your model will capture too much of the noise in
the
The subject is a Generalized Additive Model. Experts caution us against
overfitting the data, which can cause inaccurate results. I am not a
statistician (my background is in Computer Science). Perhaps some kind soul
would take a look and vet the model for overfitting the data.
The study estima
3 matches
Mail list logo
