From f7b201700221c07e45d4de2fd7d99db1a974939c Mon Sep 17 00:00:00 2001 From: Phillip Alday <palday@users.noreply.github.com> Date: Wed, 11 Dec 2024 18:10:05 +0000 Subject: [PATCH] attempt to find better starting values for GLMM when defaults aren't positive semidefinite (#796) * re-initialize to small values * simplify example * BlueStyle * use StableRNG * test * NEWS + version bump * add zenodo JSON --- .zenodo.json | 19 + NEWS.md | 5 + Project.toml | 4 +- issues/792/Manifest.toml | 843 +++++++++++++++++++++++++++++ issues/792/Project.toml | 4 + issues/792/main.jl | 13 + src/generalizedlinearmixedmodel.jl | 18 +- test/pirls.jl | 16 + 8 files changed, 914 insertions(+), 8 deletions(-) create mode 100644 .zenodo.json create mode 100644 issues/792/Manifest.toml create mode 100644 issues/792/Project.toml create mode 100644 issues/792/main.jl diff --git a/.zenodo.json b/.zenodo.json new file mode 100644 index 000000000..9e16c3711 --- /dev/null +++ b/.zenodo.json @@ -0,0 +1,19 @@ +{ + "access_right": "open", + "license": "MIT", + "title": "MixedModels.jl", + "description": "A Julia package for fitting (statistical) mixed-effects models", + "upload_type": "software", + "creators": [ + { + "affiliation": "Beacon Biosignals", + "name": "Alday, Phillip M.", + "orcid": "0000-0002-9984-5745" + }, + { + "affiliation": "University of Wisconsin - Madison", + "name": "Bates, Douglas", + "orcid": "0000-0001-8316-9503" + } + ] +} diff --git a/NEWS.md b/NEWS.md index 647d85eb5..3cdf76bf9 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +MixedModels v4.28.0 Release Notes +============================== +- `GeneralizedLinearMixedModel` now attempts to fall back to very constrained variance values when the default initial values result in a non positive semidefinite covariance matrix. [#792] + MixedModels v4.27.1 Release Notes ============================== - `profile` now includes a `finally` block to restore the original model even if an error occurs before profiling is complete [#795] @@ -579,4 +583,5 @@ Package dependencies [#783]: https://github.com/JuliaStats/MixedModels.jl/issues/783 [#785]: https://github.com/JuliaStats/MixedModels.jl/issues/785 [#791]: https://github.com/JuliaStats/MixedModels.jl/issues/791 +[#792]: https://github.com/JuliaStats/MixedModels.jl/issues/792 [#795]: https://github.com/JuliaStats/MixedModels.jl/issues/795 diff --git a/Project.toml b/Project.toml index 7c2c65bc5..e7753b994 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "MixedModels" uuid = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316" -author = ["Phillip Alday <me@phillipalday.com>", "Douglas Bates <dmbates@gmail.com>", "Jose Bayoan Santiago Calderon <jbs3hp@virginia.edu>"] -version = "4.27.1" +author = ["Phillip Alday <me@phillipalday.com>", "Douglas Bates 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+MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316" +StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3" diff --git a/issues/792/main.jl b/issues/792/main.jl new file mode 100644 index 000000000..721d2133b --- /dev/null +++ b/issues/792/main.jl @@ -0,0 +1,13 @@ +using MixedModels, DataFrames, StableRNGs +rng = StableRNG(0) +df = allcombinations(DataFrame, + "subject" => 1:10, + "session" => 1:6, + "serialpos" => 1:12) +df[!, :recalled] = rand(rng, [0, 1], nrow(df)) +serialpos_contrasts = Dict(:serialpos => DummyCoding()) + +form = @formula(recalled ~ serialpos + zerocorr(serialpos | subject) + (1 | subject & session)) +m = GeneralizedLinearMixedModel(form, df, Bernoulli(); contrasts=serialpos_contrasts); +m.optsum.ftol_rel = 1e-7 +fit!(m; init_from_lmm=[:β, :θ], fast=true) diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl index 8cf23aedd..d6b998b36 100644 --- a/src/generalizedlinearmixedmodel.jl +++ b/src/generalizedlinearmixedmodel.jl @@ -361,13 +361,10 @@ function GeneralizedLinearMixedModel( tbl, d::Distribution, l::Link=canonicallink(d); - wts=[], - offset=[], - contrasts=Dict{Symbol,Any}(), - amalgamate=true, + kwargs..., ) return GeneralizedLinearMixedModel( - f, Tables.columntable(tbl), d, l; wts, offset, contrasts, amalgamate + f, Tables.columntable(tbl), d, l; kwargs... ) end @@ -469,7 +466,16 @@ function GeneralizedLinearMixedModel( ) # if the response is constant, there's no point (and this may even fail) - constresponse || deviance!(res, 1) + constresponse || try + deviance!(res, 1) + catch ex + ex isa PosDefException || rethrow() + @warn "Evaluation at default initial parameter vector failed, " * + "initializing to very small variances. This may result in long " * + "model fitting times. You will probably also need to use " * + "`init_from_lmm=[:β, :θ]` in order to fit the model." + res.optsum.initial[res.optsum.initial .!= 0] .= 1e-8 + end return res end diff --git a/test/pirls.jl b/test/pirls.jl index 6b31245fb..1e90d9a56 100644 --- a/test/pirls.jl +++ b/test/pirls.jl @@ -265,3 +265,19 @@ end @test gm_original.optsum == gm_restored.optsum @test deviance(gm_original) ≈ deviance(gm_restored) end + +@testset "Bad initial value" begin + rng = StableRNG(0) + df = allcombinations(DataFrame, + "subject" => 1:10, + "session" => 1:6, + "serialpos" => 1:12) + df[!, :recalled] = rand(rng, [0, 1], nrow(df)) + + form = @formula(recalled ~ serialpos + zerocorr(serialpos | subject) + (1 | subject & session)) + glmm = @test_logs((:warn, r"Evaluation at default initial parameter vector failed"), + GeneralizedLinearMixedModel(form, df, Bernoulli())); + glmm.optsum.ftol_rel = 1e-7 + fit!(glmm; init_from_lmm=[:β, :θ], fast=true, progress=false) + @test deviance(glmm) ≈ 996.0402 atol=0.01 +end