Add sparseL extractor and use it in condVar

src/MixedModels.jl

@@ -75,6 +75,7 @@ export @formula,
        coeftable,
        cond,
        condVar,
+       condVartables,
        describeblocks,
        deviance,
        dispersion,
@@ -118,6 +119,7 @@ export @formula,
        setθ!,
        simulate!,
        sparse,
+       sparseL,
        std,
        stderror,
        updateL!,

src/linearmixedmodel.jl

@@ -285,15 +285,48 @@ diagonal blocks from the conditional variance-covariance matrix,
     s² Λ(Λ'Z'ZΛ + I)⁻¹Λ'
 """
 function condVar(m::LinearMixedModel{T}) where {T}
-    retrms = m.reterms
-    t1 = first(retrms)
-    L11 = first(m.L)
-    if !isone(length(retrms)) || !isa(L11, Diagonal{T,Vector{T}})
-        throw(ArgumentError("code for multiple or vector-valued r.e. not yet written"))
+    L = m.L
+    s = sdest(m)
+    @static if VERSION < v"1.6.1"
+        spL = LowerTriangular(SparseMatrixCSC{T, Int}(sparseL(m)))
+    else
+        spL = LowerTriangular(sparseL(m))
+    end
+    nre = size(spL, 1)
+    val = Array{T,3}[]
+    offset = 0
+    for (i, re) in enumerate(m.reterms)
+        λt = s * transpose(re.λ)
+        vi = size(λt, 2)
+        ℓi = length(re.levels)
+        vali = Array{T}(undef, (vi, vi, ℓi))
+        scratch = Matrix{T}(undef, (size(spL, 1), vi))
+        for b in 1:ℓi
+            fill!(scratch, zero(T))
+            copyto!(view(scratch, (offset + (b - 1) * vi) .+ (1:vi), :), λt)
+            ldiv!(spL, scratch)
+            mul!(view(vali, :, :, b), scratch', scratch)
+        end
+        push!(val, vali)
+        offset += vi * ℓi
     end
-    ll = first(t1.λ)
-    Ld = L11.diag
-    Array{T,3}[reshape(abs2.(ll ./ Ld) .* varest(m), (1, 1, length(Ld)))]
+    val
+end
+
+function _cvtbl(arr::Array{T,3}, trm) where {T}
+    merge(
+        NamedTuple{(fname(trm),)}((trm.levels,)),
+        columntable([NamedTuple{(:σ, :ρ)}(sdcorr(view(arr, :, :, i))) for i in axes(arr, 3)]),
+        )
+end
+
+"""
+    condVartables(m::LinearMixedModel)
+
+Return the conditional covariance matrices of the random effects as a `NamedTuple` of columntables
+"""
+function condVartables(m::MixedModel{T}) where {T}
+    NamedTuple{fnames(m)}((map(_cvtbl, condVar(m), m.reterms)...,))
 end
 
 function pushALblock!(A, L, blk)
@@ -910,6 +943,74 @@ end
 
 Base.show(io::IO, m::LinearMixedModel) = Base.show(io, MIME"text/plain"(), m)
 
+"""
+    _coord(A::AbstractMatrix)
+
+Return the positions and values of the nonzeros in `A` as a
+`NamedTuple{(:i, :j, :v), Tuple{Vector{Int32}, Vector{Int32}, Vector{Float64}}}`
+"""
+function _coord(A::Diagonal)
+    (i = Int32.(axes(A,1)), j = Int32.(axes(A,2)), v = A.diag)
+end
+
+function _coord(A::UniformBlockDiagonal)
+    dat = A.data
+    r, c, k = size(dat)
+    blk = repeat(r .* (0:k-1), inner=r*c)
+    (
+        i = Int32.(repeat(1:r, outer=c*k) .+ blk),
+        j = Int32.(repeat(1:c, inner=r, outer=k) .+ blk),
+        v = vec(dat)
+    )
+end
+
+function _coord(A::SparseMatrixCSC{T,Int32}) where {T}
+    rv = rowvals(A)
+    cv = similar(rv)
+    for j in axes(A, 2), k in nzrange(A, j)
+        cv[k] = j
+    end
+    (i = rv, j = cv, v = nonzeros(A), )
+end
+
+function _coord(A::Matrix)
+    m, n = size(A)
+    (
+        i = Int32.(repeat(axes(A, 1), outer=n)),
+        j = Int32.(repeat(axes(A, 2), inner=m)),
+        v = vec(A),
+    )
+end
+
+"""
+    sparseL(m::LinearMixedModel{T}; full::Bool=false) where {T}
+
+Return the lower Cholesky factor `L` as a `SparseMatrix{T,Int32}`.
+
+`full` indicates whether the parts of `L` associated with the fixed-effects and response
+are to be included.
+"""
+function sparseL(m::LinearMixedModel{T}; full::Bool=false) where {T}
+    L, reterms = m.L, m.reterms
+    nt = length(reterms) + full
+    rowoffset, coloffset = 0, 0
+    val = (i = Int32[], j = Int32[], v = T[])
+    for i in 1:nt, j in 1:i
+        Lblk = L[block(i, j)]
+        cblk = _coord(Lblk)
+        append!(val.i, cblk.i .+ Int32(rowoffset))
+        append!(val.j, cblk.j .+ Int32(coloffset))
+        append!(val.v, cblk.v)
+        if i == j
+            coloffset = 0
+            rowoffset += size(Lblk, 1)
+        else
+            coloffset += size(Lblk, 2)
+        end
+    end
+    dropzeros!(tril!(sparse(val...,)))
+end
+
 
 """
     ssqdenom(m::LinearMixedModel)

src/utilities.jl

@@ -25,7 +25,7 @@ end
 
 Return the average of `a` and `b`
 """
-average(a::T, b::T) where {T<:AbstractFloat} = (a + b) / 2
+average(a::T, b::T) where {T<:AbstractFloat} = (a + b) / T(2)
 
 """
     cpad(s::AbstractString, n::Integer)
@@ -137,3 +137,27 @@ function replicate(f::Function, n::Integer;
     end
     results
 end
+
+"""
+    sdcorr(A::AbstractMatrix{T}) where {T}
+
+Transform a square matrix `A` with positive diagonals into an `NTuple{size(A,1), T}` of
+standard deviations and a tuple of correlations.
+
+`A` is assumed to be symmetric and only the lower triangle is used.  The order of the
+correlations is row-major ordering of the lower triangle (or, equivalently, column-major
+in the upper triangle).
+"""
+function sdcorr(A::AbstractMatrix{T}) where {T}
+    m,n = size(A)
+    m == n || throw(ArgumentError("matrix A must be square"))
+    indpairs = checkindprsk(m)
+    rtdiag = sqrt.(NTuple{m,T}(diag(A)))
+    (
+        rtdiag,
+        ntuple(kchoose2(m)) do k
+            i,j = indpairs[k]
+            A[i,j]/(rtdiag[i] * rtdiag[j])
+        end,
+    )
+end

test/bootstrap.jl

@@ -3,6 +3,7 @@ using LinearAlgebra
 using MixedModels
 using Random
 using StableRNGs
+using Statistics
 using Tables
 using Test
 
@@ -34,7 +35,6 @@ include("modelcache.jl")
         # restore the original state
         refit!(fm, vec(float.(ds.yield)))
     end
-
     @testset "Poisson" begin
         center(v::AbstractVector) = v .- (sum(v) / length(v))
         grouseticks = DataFrame(dataset(:grouseticks))
@@ -50,7 +50,6 @@ include("modelcache.jl")
         gm2sim = refit!(simulate!(StableRNG(42), deepcopy(gm2)), fast=true)
         @test isapprox(gm2.β, gm2sim.β; atol=norm(stderror(gm2)))
     end
-
     @testset "_rand with dispersion" begin
         @test_throws ArgumentError MixedModels._rand(StableRNG(42), Normal(), 1, 1, 1)
         @test_throws ArgumentError MixedModels._rand(StableRNG(42), Gamma(), 1, 1, 1)
@@ -99,7 +98,6 @@ end
         @test sum(issingular(bsamp)) == sum(issingular(bsamp_threaded))
     end
 
-
     @testset "Bernoulli simulate! and GLMM boostrap" begin
         contra = dataset(:contra)
         gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=true)

test/likelihoodratiotest.jl

@@ -85,7 +85,6 @@ end
     fm1 = fit(MixedModel,@formula(reaction ~ 1 + days + (1+days|subj)),slp, REML=true);
 
     @test_throws ArgumentError likelihoodratiotest(fm0,fm1)
-
     contra = MixedModels.dataset(:contra);
     # glm doesn't like categorical responses, so we convert it to numeric ourselves
     # TODO: upstream fix

test/mime.jl

@@ -6,7 +6,6 @@ using MixedModels: pirls!, setβθ!, setθ!, updateL!
 
 include("modelcache.jl")
 
-
 # explicitly setting theta for these to so that we can do exact textual comparisons
 βθ = [0.1955554704948119,  0.05755412761885973, 0.3207843518569843, -1.0582595252774376,
      -2.1047524824609853, -1.0549789653925743,  1.339766125847893,  0.4953047709862237]
@@ -32,7 +31,6 @@ lrt = likelihoodratiotest(fm0, fm1)
     mime = MIME"text/markdown"()
     @test_logs (:warn, "Model has not been fit: results will be nonsense") sprint(show, mime, gm3)
     gm3.optsum.feval = 1
-
     @testset "lmm" begin
         @test sprint(show, mime, fm0) == """
 |             |     Est. |     SE |     z |      p |  σ_subj |
@@ -153,7 +151,6 @@ end
 @testset "html" begin
     # this is minimal since we're mostly testing that dispatch works
     # the stdlib actually handles most of the conversion
-
     @test sprint(show, MIME("text/html"), BlockDescription(gm3)) == """
 <table><tr><th align="left">rows</th><th align="left">subj</th><th align="left">item</th><th align="left">fixed</th></tr><tr><td align="left">316</td><td align="left">Diagonal</td><td align="left"></td><td align="left"></td></tr><tr><td align="left">24</td><td align="left">Dense</td><td align="left">Diag/Dense</td><td align="left"></td></tr><tr><td align="left">7</td><td align="left">Dense</td><td align="left">Dense</td><td align="left">Dense</td></tr></table>
 """

test/pls.jl

@@ -166,7 +166,12 @@ end
     @test varest(fm) ≈ 0.3024263987592062 atol=0.0001
     @test logdet(fm) ≈ 95.74614821367786 atol=0.001
 
-    @test_throws ArgumentError condVar(fm)
+    cv = condVar(fm)
+    @test length(cv) == 2
+    @test size(first(cv)) == (1, 1, 24)
+    @test size(last(cv)) == (1, 1, 6)
+    @test first(first(cv)) ≈ 0.07331320237988301 rtol=1.e-4
+    @test last(last(cv)) ≈ 0.04051547211287544 rtol=1.e-4
 
     rfu = ranef(fm, uscale=true)
     @test length(rfu) == 2
@@ -200,6 +205,13 @@ end
     @test varest(fm) ≈ 0.6780020742644107 atol=0.0001
     @test logdet(fm) ≈ 101.0381339953986 atol=0.001
 
+    cv = condVar(fm)
+    @test length(cv) == 2
+    @test size(first(cv)) == (1, 1, 30)
+    @test first(first(cv)) ≈ 1.111873335663485 rtol=1.e-4
+    @test size(last(cv)) == (1, 1, 10)
+    @test last(last(cv)) ≈ 0.850428770978789 rtol=1.e-4
+
     show(io, BlockDescription(fm))
     @test countlines(seekstart(io)) == 4
     tokens = Set(split(String(take!(io)), r"\s+"))
@@ -217,6 +229,10 @@ end
     @test fm1.optsum.initial == ones(3)
     @test lowerbd(fm1) == zeros(3)
 
+    spL = sparseL(fm1)
+    @test size(spL) == (4114, 4114)
+    @test 733090 < nnz(spL) < 733100
+
     @test objective(fm1) ≈ 237721.7687745563 atol=0.001
     ftd1 = fitted(fm1);
     @test size(ftd1) == (73421, )
@@ -291,6 +307,29 @@ end
     @test size(first(u3)) == (2, 18)
     @test first(u3)[1, 1] ≈ 3.030300122575336 atol=0.001
 
+    cv = condVar(fm)
+    @test length(cv) == 1
+    @test size(first(cv)) == (2, 2, 18)
+    @test first(first(cv)) ≈ 140.96612241084617 rtol=1.e-4
+    @test last(last(cv)) ≈ 5.157750215432247 rtol=1.e-4
+    @test first(cv)[2] ≈ -20.60428045516186 rtol=1.e-4
+
+    cvt = condVartables(fm)
+    @test length(cvt) == 1
+    @test only(keys(cvt)) == :subj
+    cvtsubj = cvt.subj
+    @test only(cvt) === cvtsubj
+    @test keys(cvtsubj) == (:subj, :σ, :ρ)
+    @test Tables.istable(cvtsubj)
+    @test first(cvtsubj.subj) == "S308"
+    cvtsubjσ1 = first(cvtsubj.σ)
+    @test all(==(cvtsubjσ1), cvtsubj.σ)
+    @test first(cvtsubjσ1) ≈ 11.87291549750297 atol=1.0e-4
+    @test last(cvtsubjσ1) ≈ 2.271068078114843 atol=1.0e-4
+    cvtsubjρ = first(cvtsubj.ρ)
+    @test all(==(cvtsubjρ), cvtsubj.ρ)
+    @test only(cvtsubjρ) ≈ -0.7641347018831385 atol=1.0e-4
+
     b3 = ranef(fm)
     @test length(b3) == 1
     @test size(first(b3)) == (2, 18)