subsetdims: support full coordinate permutations (p == d)

src/Subsetting.jl

@@ -81,7 +81,9 @@ This function extracts the dependence structure among the specified dimensions f
 function subsetdims(C::Copula{d},dims::NTuple{p, Int}) where {d,p}
     p==1 && return Distributions.Uniform()
     dims==ntuple(i->i, d) && return C
-    @assert p < d
+    # p == d is allowed: a `dims` that is a (non-identity) permutation reorders the
+    # coordinates. The identity `dims == 1:d` is already returned above.
+    @assert p <= d
     @assert length(unique(dims))==length(dims)
     @assert all(dims .<= d)
     return SubsetCopula(C,dims)

test/Subsetting.jl

@@ -0,0 +1,38 @@
+# Full-permutation `subsetdims` (p == d). Reordering *all* coordinates with a
+# non-identity permutation of `1:d` previously threw `@assert p < d`; it now
+# returns the correctly-reordered copula. (The identity `dims == 1:d` still
+# short-circuits to the original copula, and `p == 1` to a `Uniform`.)
+@testset "subsetdims full permutation (p == d)" begin
+
+    # Ground truth: cdf(subsetdims(C, perm), u) == cdf(C, v) with v[perm[i]] = u[i].
+    permuted_point(perm, u) = (v = similar(u); for (i, j) in enumerate(perm); v[j] = u[i]; end; v)
+
+    @testset "regression: p == d no longer throws" begin
+        @test Copulas.subsetdims(ClaytonCopula(3, 2.0), (2, 3, 1)) isa Copulas.Copula
+        @test Copulas.subsetdims(GaussianCopula([1.0 0.5 0.2; 0.5 1.0 0.3; 0.2 0.3 1.0]), (3, 1, 2)) isa Copulas.Copula
+    end
+
+    @testset "Archimedean (exchangeable) agrees with the parent" begin
+        for C in (ClaytonCopula(3, 2.0), FrankCopula(4, 3.0))
+            d = length(C); perm = ntuple(i -> mod1(i + 1, d), d)        # cyclic shift (non-identity)
+            S = Copulas.subsetdims(C, perm)
+            for _ in 1:5
+                u = rand(rng, d)
+                @test cdf(S, u)    ≈ cdf(C, permuted_point(perm, u))    atol = 1e-8
+                @test logpdf(S, u) ≈ logpdf(C, permuted_point(perm, u)) atol = 1e-8
+            end
+        end
+    end
+
+    @testset "Gaussian (asymmetric Σ — permutation is non-trivial)" begin
+        Σ = [1.0 0.6 0.2; 0.6 1.0 0.5; 0.2 0.5 1.0]
+        C = GaussianCopula(Σ); perm = (2, 3, 1)
+        S = Copulas.subsetdims(C, perm)
+        @test S.Σ ≈ Σ[collect(perm), collect(perm)]                     # the reordered correlation matrix
+        for _ in 1:5
+            u = rand(rng, 3)
+            @test logpdf(S, u) ≈ logpdf(C, permuted_point(perm, u)) atol = 1e-8
+            @test cdf(S, u)    ≈ cdf(C, permuted_point(perm, u))    atol = 1e-2   # MvNormalCDF is Monte-Carlo
+        end
+    end
+end

test/runtests.jl

@@ -14,6 +14,7 @@ testfiles = [
     "FittingTest",
     "MiscelaneousCopulas",
     "SklarDist",
+    "Subsetting",
     "GenericTests",
 ]