I am having a difficult time interpreting the gam.plots produced by the
plot() function in the package
mgcv in R—specifically, plots from an ordered regression model using the family
ocat. I know how to interpret the plots when i'm doing a usual gaussian or glm-family gam. It is specifically the ordinal regression that is tricky for me.
Here is an example of the gam.plot that is produced based upon the code in the R documentation (https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/ocat.html):
The documentation says: "Family for use with gam, implementing regression for ordered categorical data. A linear predictor provides the expected value of a latent variable following a logistic distribution. The probability of this latent variable lying between certain cut-points provides the probability of the ordered categorical variable being of the corresponding category."
At the same time it also says that the link function is the identity function.
I don't know if it makes any sense to transform the response in some way? If not, how should I interpret these plots?
Maybe I should also add that when I fit a cummulative-link model in R using the
clmm() function from the
ordinal package the regression models use a 'logit' link function, and require you to exponentiate the b-coeffecients to get interpretable results. I suspect this is not the case with gams.