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How to interpret GAM summary in are for Poisson family?
The summary should be understandable when you know what a GLM and a GAM is and what spline is and how they are evaluated. If you don’t know the theory behind (that cannot be explained here, but you should find plenty of literature), the output will remain somewhat cryptic.
Why do I have problems interpreting GAM output?
This is most likely due to your model not being the correct way (in mgcvat least) to fit a model like this. First off, we need to note that the basis expansion of Yearcontains a linear function.
How can I interpret these figure from GAM analysis?
This enables you to easily see what data points exist for each variable and something about how they are distributed. Perhaps the most interesting looking GAM curve fitted is for S (salinity) which has a large dip fitted between around 30 and 100 units of salinity.
How does Generalized Additive Model ( GAM ) work?
Rather than fitting multiple variables simultaneously, the algorithm fits a smooth curve to each variable and then combines the results additively, thus giving rise to the name Generalized Additive Models.’ (in http://ecology.msu.montana.edu/labdsv/R/labs/lab5/lab5.html)
Is the GAM summary of a model important?
For a GAM this summary is not that important, particularily your sample size is rather large given the relatively simple model (p-values should not be relevant).
Which is better for gamms BAM or gamm4?
For fitting GAMMs with modest numbers of i.i.d. random coefficients then gamm4 is slower than gam (or bam for large data sets). gamm4 is most useful when the random effects are not i.i.d., or when there are large numbers of random coeffecients (more than several hundred), each applying to only a small proportion of the response data.
How is smoothness selection done in gamm4 R?
For earlier lme4 versions modelling fitting is via a call to lmer in the normal errors identity link case, or by a call to glmer otherwise (see lmer ). Smoothness selection is by REML in the Gaussian additive case and (Laplace approximate) ML otherwise.