Closed caibengbu closed 3 years ago
Great! Could you also add the following:
@test
(see e.g. test/nlreg.jl
)Merging #26 (115e138) into master (28b55d7) will increase coverage by
3.04%
. The diff coverage is100.00%
.
@@ Coverage Diff @@
## master #26 +/- ##
==========================================
+ Coverage 76.95% 80.00% +3.04%
==========================================
Files 12 13 +1
Lines 499 565 +66
==========================================
+ Hits 384 452 +68
+ Misses 115 113 -2
Impacted Files | Coverage Δ | |
---|---|---|
src/GLFixedEffectModels.jl | 0.00% <ø> (ø) |
|
src/utils/biascorr.jl | 100.00% <100.00%> (ø) |
|
src/fit.jl | 82.19% <0.00%> (+1.04%) |
:arrow_up: |
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Thank you, Johannes. I updated the test script. The two-way Logit test was fine but the coefficient didn't match for models that use a Probit link.
I use RCall
to get the result from alpaca::feglm
and compare the result with our function nlreg
. They appear to be different before bias correction with atol = 1e-4
. It might be a matter of tolerance level or other stopping criteria since the gap is almost negligible. I'll look more into it. If you can provide some suggestions, it'll be very helpful as well.
Thank you. That's concerning-- yes, it would be great if you could look into what's causing the difference in the probit regressions (I should have really added these to the tests in the first place...). But that can go into a different PR, so feel free to comment out the probit test stuff for now.
By the way, to have biascorr_test.jl also run by the Github CI, you also need to add the file in test/runtests.jl
. If you want to use some other dependencies in that script that are not already listed in Project.toml
(like RCall
), you'll have to add it to the test vector, otherwise the tests will fail.
[targets]
test = ["Test", "Random", "Statistics", "RDatasets", "StableRNGs"]
Feel free to message me on Slack if the tests keep failing.
Awesome, thank you!
This pull request adds the bias correction feature to the GLFixedEffectModels package. It returns the same numerical results with
alpaca::BiasCorr()
in R.TO-DO: