Skip to content

Make predict return same table type as input #606

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Make predict return same table type as input #606

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
bdd1ad0
Make `predict` return same table type as input
nalimilan Apr 30, 2025
0c2fb7a
Update test/runtests.jl
nalimilan Apr 30, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion src/linpred.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -383,6 +383,6 @@ function StatsBase.predict(mm::LinPredModel, data;
upper=view(upper, nonmissinginds))
predict!(tup, mm, newx;
interval=interval, kwargs...)
return (prediction=prediction, lower=lower, upper=upper)
return Tables.materializer(data)((prediction=prediction, lower=lower, upper=upper))
end
end
14 changes: 9 additions & 5 deletions test/runtests.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1048,6 +1048,7 @@ end
@test gm11_pred1 == gm11_pred2.prediction == gm11_pred3.prediction ≈ newY
J = newX .* getindex.(GLM.inverselink.(LogitLink(), newX*coef(gm11)), 2)
se_pred = sqrt.(diag(J*vcov(gm11)*J'))
@test gm11_pred2 isa NamedTuple
@test gm11_pred2.lower ≈ gm11_pred2.prediction .- quantile(Normal(), 0.975) .* se_pred ≈
[0.20478201781547786, 0.2894172253195125, 0.17487705636545708,
0.024943206131575357, 0.41670326978944977]
Expand Down Expand Up @@ -1113,10 +1114,11 @@ end

newd = DataFrame(newX, :auto)
@test predict(gm13, newd) == predict(gm13, newX)
@test predict(gm13, newX; interval=:confidence, interval_method=:delta) ==
predict(gm11, newX; interval=:confidence, interval_method=:delta)
@test predict(gm13, newd; interval=:confidence, interval_method=:delta) ==
DataFrame(predict(gm11, newX; interval=:confidence, interval_method=:delta))
@test predict(gm13, newd; interval=:confidence, interval_method=:transformation) ==
predict(gm11, newX; interval=:confidence, interval_method=:transformation)
DataFrame(predict(gm11, newX; interval=:confidence,
interval_method=:transformation))

# Prediction from DataFrames with missing values
drep = d[[1, 2, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10], :]
Expand Down Expand Up @@ -1161,6 +1163,7 @@ end
pred2 = predict(mm, newX, interval=:confidence)
se_pred = sqrt.(diag(newX*vcov(mm)*newX'))

@test pred2 isa NamedTuple
@test pred1 == pred2.prediction ≈
[1.1382137814295972, 1.2097057044789292, 1.7983095679661645, 1.0139576473310072,
0.9738243263215998]
Expand All @@ -1180,6 +1183,7 @@ end
@test ndims(pred2.upper) == 1

pred3 = predict(mm, newX, interval=:prediction)
@test pred3 isa NamedTuple
@test pred1 == pred3.prediction ≈
[1.1382137814295972, 1.2097057044789292, 1.7983095679661645, 1.0139576473310072,
0.9738243263215998]
Expand Down Expand Up @@ -1236,9 +1240,9 @@ end
newd = DataFrame(newX, :auto)
@test predict(mmd, newd) == predict(mm, newX)
@test predict(mmd, newd; interval=:confidence) ==
predict(mm, newX; interval=:confidence)
DataFrame(predict(mm, newX; interval=:confidence))
@test predict(mmd, newd; interval=:prediction) ==
predict(mm, newX; interval=:prediction)
DataFrame(predict(mm, newX; interval=:prediction))

# Prediction from DataFrames with missing values
drep = d[[1, 2, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10], :]
Expand Down
Loading