Add linearpredictor and linearpredictor!

[ararslan]

I've added a default definition for linearpredictor that requires that a model has implemented modelmatrix, coef, and offset. I did not add a default definition nor documented signature for linearpredictor!.

As separate commits, I've also bumped the version to 1.5.0 for a feature release (assuming this is desirable) and ignored Manifest.toml in the .gitignore.

[codecov-commenter]

Codecov Report

Merging #19 (a634719) into main (e42a88a) will not change coverage.
The diff coverage is n/a.

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Impacted Files	Coverage Δ
src/regressionmodel.jl	100.00% <ø> (ø)

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[nalimilan]

What does updating model directly do? Do you have any examples of this?

[ararslan]

I was thinking of a case in which the relevant storage lives inside the model object itself. BetaRegression does this, as does GLM, though GLM exposes it in a roundabout way by passing the field as the first argument. I could also imagine a situation where updating a model directly involves mutating multiple internal fields, in which case you couldn't pass only the mutated field as the first argument since there'd be more than one.

[nalimilan]

But when the linear predictor is already stored in the model, linearpredictor can just return it directly, there's no need to call that operation linearpredictor!, right?

[ararslan]

I guess what I was thinking of is if you store the model matrix, coefficients, and linear predictor in the model object, you'd call linearpredictor! to basically mul!(linearpredictor(model), modelmatrix(model), coef(model)) or whatever is appropriate for the model. GLM calls this linpred! but as I said exposes the update a bit differently even though everything actually is stored in the model object. So perhaps it's my description in the docstring that's inaccurate (or at least imprecise) in terms of what I meant with this function.

[nalimilan]

OK. I still don't really see what linearpredictor!(model) is supposed to do. :-)

I imagine the idea is to follow the pattern used here?
https://github.com/ararslan/BetaRegression.jl/blob/beb83b5ad0c9acbf8cee19138395a49d66be774e/src/BetaRegression.jl#L150
If so, my suggestion is to simply use linearpredictor(model) for that, as the fact that internal fields are mutated is an implementation detail (as long as users are clearly told that the returned vector is owned by the model).

[ararslan]

Yeah, that's the pattern I was referring to, which I was imagining could similarly be adopted in GLM to simplify some calls. For BetaRegression, as long as the linear predictor is stored in the model I want to keep linearpredictor purely as an accessor as it's used that way extensively (currently as linpred) inside the package. In the meantime I've removed the bit in the documentation here about a single argument.

[nalimilan]

OK, let's go with with this simple version for now then.