Not sure how clear the title is or whether this is the right place for asking the following question, but any way…
I am testing the GLM package mostly for educational purposes. In the package documentation I saw that the lm function is an alias to fit(LinearModel,...) and glm for fit(GeneralizedLinearModel). However, when I compare the two functions, I get a false statement. For instance,
I’m not exactly sure what this comparison does but glms are maximum likelihood estimations so you would have to set a seed I think. Is this the same for lm? Of course numerical differences may still persist
so it is not clear what it does exactly for this type. One would have to check the source of the GLM package if that defines a specific ==. There is a fallback I think:
from the docs:
search: isequal issetequal InverseSquareLink
Similar to ==, except for the treatment of floating point numbers and of missing values. isequal treats all floating-point NaN values as equal to each other, treats -0.0 as
unequal to 0.0, and missing as equal to missing. Always returns a Bool value.
The default implementation of isequal calls ==, so a type that does not involve floating-point values generally only needs to define ==.
isequal is the comparison function used by hash tables (Dict). isequal(x,y) must imply that hash(x) == hash(y).
This typically means that types for which a custom == or isequal method exists must implement a corresponding hash method (and vice versa). Collections typically implement isequal
by calling isequal recursively on all contents.
Scalar types generally do not need to implement isequal separate from ==, unless they represent floating-point numbers amenable to a more efficient implementation than that
provided as a generic fallback (based on isnan, signbit, and ==).
Looks like the fallback is called which would not work I guess
julia> @which isequal(probit1, probit2)
isequal(x, y) in Base at operators.jl:123
By default == falls back to ===, and I don’t think anybody cared about implementing == for GLMs. So two different fits will always be considered as different. PR welcome to implement a smarter behavior (by calling == recursively on relevant fields).