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add residuals method for GLM #499

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8625214
add residuals method for GLM
palday Sep 20, 2022
30a77ab
set default to deviance residuals
palday Sep 20, 2022
92660be
Apply suggestions from code review
palday Sep 21, 2022
ca6342b
restore whitespace in f-tests
palday Sep 21, 2022
66325eb
add in tests for other families
palday Sep 21, 2022
ffd7c97
update doctests
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Merge branch 'master' of github.com:JuliaStats/GLM.jl into pa/resid
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Merge branch 'master' of github.com:JuliaStats/GLM.jl into pa/resid
palday Nov 11, 2022
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Merge branch 'master' of github.com:JuliaStats/GLM.jl into pa/resid
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613f5fe
add code for pearson
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Merge branch 'master' of github.com:JuliaStats/GLM.jl into pa/resid
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whitespace
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whitespace
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whitespace
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again
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cfa33e0
Geometric and negative binomial
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ee87fc7
residuals for normal
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Update test/runtests.jl
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Merge branch 'master' of github.com:JuliaStats/GLM.jl into pa/resid
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Update src/glmfit.jl
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34 changes: 34 additions & 0 deletions src/glmfit.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -731,3 +731,37 @@ function checky(y, d::Binomial)
end
return nothing
end

# need to add :pearson
const _RESIDUAL_TYPES = [:deviance, :response, :working]
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"""
residuals(model::GeneralizedLinearModel; type=:deviance)

Return the residuals of a GLM.

Supported residual types are:
- `:response`: the difference between the observed and fitted values
- `:deviance`: the signed square root of the elementwise
contribution to the deviance
- `:working`: working residuals (used during the IRLS process)
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"""
function residuals(model::GeneralizedLinearModel; type=:deviance)
type in _RESIDUAL_TYPES ||
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Why not use the else in the conditional to catch unsupported types rather than maintaining a separate list of types? Users can consult the docstring if they're not sure what's supported. The only other place the constant is used is in the tests where you loop over it. You could just move the list there where it's needed.

throw(ArgumentError("Unsupported type `$(type)``; supported types are" *
"$(_RESIDUAL_TYPES)"))
# TODO: add in optimized method for normal with identity linnk
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res = if type == :response
response(model) - fitted(model)
elseif type == :deviance
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# XXX I think this might be the same as
# 2 * wrkresid, but I'm not 100% sure if that holds across families
Comment on lines +867 to +868
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@dmbates Does this relationship hold across families?

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Can you use the devresid function here?

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No, it seems that that function is lower level and used in the computation of the associated field rather returning said field.

sign.(response(model) .- fitted(model)) .* sqrt.(model.rr.devresid)
elseif type == :working
return model.rr.wrkresid
else
error("An error has occurred. Please file an issue on GitHub.")
end

return res
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end
2 changes: 1 addition & 1 deletion src/glmtools.jl
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Expand Up @@ -514,7 +514,7 @@ function loglik_obs end
loglik_obs(::Bernoulli, y, μ, wt, ϕ) = wt*logpdf(Bernoulli(μ), y)
loglik_obs(::Binomial, y, μ, wt, ϕ) = logpdf(Binomial(Int(wt), μ), Int(y*wt))
loglik_obs(::Gamma, y, μ, wt, ϕ) = wt*logpdf(Gamma(inv(ϕ), μ*ϕ), y)
# In Distributions.jl, a Geometric distribution characterizes the number of failures before
# In Distributions.jl, a Geometric distribution characterizes the number of failures before
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# the first success in a sequence of independent Bernoulli trials with success rate p.
# The mean of Geometric distribution is (1 - p) / p.
# Hence, p = 1 / (1 + μ).
Expand Down
63 changes: 44 additions & 19 deletions test/runtests.jl
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Expand Up @@ -51,22 +51,22 @@ linreg(x::AbstractVecOrMat, y::AbstractVector) = qr!(simplemm(x)) \ y
end

@testset "Linear Model Cook's Distance" begin
st_df = DataFrame(
st_df = DataFrame(
Y=[6.4, 7.4, 10.4, 15.1, 12.3 , 11.4],
XA=[1.5, 6.5, 11.5, 19.9, 17.0, 15.5],
XB=[1.8, 7.8, 11.8, 20.5, 17.3, 15.8],
XB=[1.8, 7.8, 11.8, 20.5, 17.3, 15.8],
XC=[3., 13., 23., 39.8, 34., 31.],
# values from SAS proc reg
CooksD_base=[1.4068501943, 0.176809102, 0.0026655177, 1.0704009915, 0.0875726457, 0.1331183932],
CooksD_noint=[0.0076891801, 0.0302993877, 0.0410262965, 0.0294348488, 0.0691589296, 0.0273045538],
CooksD_base=[1.4068501943, 0.176809102, 0.0026655177, 1.0704009915, 0.0875726457, 0.1331183932],
CooksD_noint=[0.0076891801, 0.0302993877, 0.0410262965, 0.0294348488, 0.0691589296, 0.0273045538],
CooksD_multi=[1.7122291956, 18.983407026, 0.000118078, 0.8470797843, 0.0715921999, 0.1105843157],
)

# linear regression
t_lm_base = lm(@formula(Y ~ XA), st_df)
@test isapprox(st_df.CooksD_base, cooksdistance(t_lm_base))

# linear regression, no intercept
# linear regression, no intercept
t_lm_noint = lm(@formula(Y ~ XA +0), st_df)
@test isapprox(st_df.CooksD_noint, cooksdistance(t_lm_noint))

Expand All @@ -82,7 +82,7 @@ end

end

@testset "linear model with weights" begin
@testset "linear model with weights" begin
df = dataset("quantreg", "engel")
N = nrow(df)
df.weights = repeat(1:5, Int(N/5))
Expand All @@ -94,13 +94,17 @@ end
@test isapprox(stderror(lm_model), [9.382302620120193, 0.00816741377772968])
@test isapprox(r2(lm_model), 0.8330258148644486)
@test isapprox(adjr2(lm_model), 0.832788298242634)
@test isapprox(vcov(lm_model), [88.02760245551447 -0.06772589439264813;
@test isapprox(vcov(lm_model), [88.02760245551447 -0.06772589439264813;
-0.06772589439264813 6.670664781664879e-5])
@test isapprox(first(predict(lm_model)), 357.57694841780994)
@test isapprox(loglikelihood(lm_model), -4353.946729075838)
@test isapprox(loglikelihood(glm_model), -4353.946729075838)
@test isapprox(nullloglikelihood(lm_model), -4984.892139711452)
@test isapprox(mean(residuals(lm_model)), -5.412966629787718)
@test isapprox(mean(residuals(lm_model)), -5.412966629787718)

# this should be elementwise true (which is a stronger condition than
# vectors being approximately equal) the so we test elementwise
@test all(residuals(glm_model; type=:response) .≈ residuals(lm_model))
end

@testset "rankdeficient" begin
Expand Down Expand Up @@ -315,6 +319,27 @@ admit.rank = categorical(admit.rank)
@test isapprox(coef(gm2),
[-3.9899786606380756, 0.0022644256521549004, 0.804037453515578,
-0.6754428594116578, -1.340203811748108, -1.5514636444657495])
res = residuals(gm2; type=:deviance)
# values from R
@test isapprox(res[1:10],
[-0.6156283, 1.568695, 0.7787919, 1.856779, -0.5019254,
1.410201, 1.318558, -0.6994666, 1.792076, -1.207922]; atol=1e-6)
@test isapprox(res[390:400],
[-1.015303, 1.352001, 1.199244, 1.734904, 1.283653, 1.656921,
-1.158223, -0.6015442, -0.6320556, -1.116244, -0.8458358]; atol=1e-6)
res = residuals(gm2; type=:response)
# values from R
@test isapprox(res[1:10],
[-0.1726265, 0.707825, 0.2615918, 0.8216154, -0.1183539,
0.6300301, 0.5807538, -0.2170033, 0.7992648, -0.5178682], atol=1e-6)
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@test isapprox(res[390:400],
[-0.4027505, 0.5990641, 0.512806, 0.7779709, 0.5612748,
0.7465767, -0.48867, -0.1655043, -0.1810622, -0.4636674, -0.3007306]; atol=1e-6)
# less extensive test here because it's just grabbing the IRLS values, which are already
# extensively tested
@test isapprox(residuals(gm2; type=:working)[1:10],
[-1.208644, 3.422607, 1.354264, 5.605865, -1.134242,
2.702922, 2.385234, -1.277145, 4.981688, -2.074122]; atol=1e-5)
end

@testset "Bernoulli ProbitLink" begin
Expand Down Expand Up @@ -707,8 +732,8 @@ end
@test aic(gm22) ≈ 1112.7422553284146
@test aicc(gm22) ≈ 1113.7933502189255
@test bic(gm22) ≈ 1136.6111083020812
@test coef(gm22)[1:7] ≈ [2.8978546663153897, -0.5701067649409168, 0.08040181505082235,
-0.4497584898742737, 0.08622664933901254, 0.3558996662512287,
@test coef(gm22)[1:7] ≈ [2.8978546663153897, -0.5701067649409168, 0.08040181505082235,
-0.4497584898742737, 0.08622664933901254, 0.3558996662512287,
0.29016080736927813]
@test stderror(gm22) ≈ [0.22754287093719366, 0.15274755092180423, 0.15928431669166637,
0.23853372776980591, 0.2354231414867577, 0.24750780320597515,
Expand Down Expand Up @@ -823,7 +848,7 @@ end
gm11 = fit(GeneralizedLinearModel, X, Y, Binomial())
@test isapprox(predict(gm11), Y)
@test predict(gm11) == fitted(gm11)

newX = rand(rng, 5, 2)
newY = logistic.(newX * coef(gm11))
gm11_pred1 = predict(gm11, newX)
Expand Down Expand Up @@ -1055,7 +1080,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 3 0.1283 0.9603
[1] 3 0.1283 0.9603
[2] 2 -1 3.2292 3.1008 -0.0000 -0.9603 241.6234 <1e-07
──────────────────────────────────────────────────────────────────"""
else
Expand All @@ -1064,7 +1089,7 @@ end
─────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
─────────────────────────────────────────────────────────────────
[1] 3 0.1283 0.9603
[1] 3 0.1283 0.9603
[2] 2 -1 3.2292 3.1008 -0.0000 -0.9603 241.6234 <1e-7
─────────────────────────────────────────────────────────────────"""
end
Expand All @@ -1078,7 +1103,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 2 3.2292 -0.0000
[1] 2 3.2292 -0.0000
[2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-07
──────────────────────────────────────────────────────────────────"""
else
Expand All @@ -1087,7 +1112,7 @@ end
─────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
─────────────────────────────────────────────────────────────────
[1] 2 3.2292 -0.0000
[1] 2 3.2292 -0.0000
[2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-7
─────────────────────────────────────────────────────────────────"""
end
Expand All @@ -1103,7 +1128,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 2 3.2292 -0.0000
[1] 2 3.2292 -0.0000
[2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-07
[3] 5 2 0.1017 -0.0266 0.9685 0.0082 1.0456 0.3950
──────────────────────────────────────────────────────────────────"""
Expand All @@ -1113,7 +1138,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 2 3.2292 -0.0000
[1] 2 3.2292 -0.0000
[2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-7
[3] 5 2 0.1017 -0.0266 0.9685 0.0082 1.0456 0.3950
──────────────────────────────────────────────────────────────────"""
Expand All @@ -1129,7 +1154,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 5 0.1017 0.9685
[1] 5 0.1017 0.9685
[2] 3 -2 0.1283 0.0266 0.9603 -0.0082 1.0456 0.3950
[3] 2 -1 3.2292 3.1008 -0.0000 -0.9603 241.6234 <1e-07
──────────────────────────────────────────────────────────────────"""
Expand All @@ -1139,7 +1164,7 @@ end
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
──────────────────────────────────────────────────────────────────
[1] 5 0.1017 0.9685
[1] 5 0.1017 0.9685
[2] 3 -2 0.1283 0.0266 0.9603 -0.0082 1.0456 0.3950
[3] 2 -1 3.2292 3.1008 -0.0000 -0.9603 241.6234 <1e-7
──────────────────────────────────────────────────────────────────"""
Expand Down