Add data and script for issue 833

issues/705/705.jl

@@ -0,0 +1,39 @@
+using PRIMA, MixedModels, CSV, DataFrames, StatsBase, Random
+
+saddlepointdata = CSV.read("saddlepointdata.csv", DataFrame)
+transform!(saddlepointdata, "AUCT" => eachindex => "row")
+
+N = 500
+_rng = Random.seed!(Random.default_rng(), 124)
+fts = map(1:N) do _
+    m = LinearMixedModel(
+        @formula(log(AUCT) ~
+            trt +
+            seq +
+            per +
+            (trt + 0 | sub) +
+            zerocorr(trt + 0 | row)
+        ),
+        transform(
+            saddlepointdata,
+            "AUCT" => ByRow(t -> t + randn(_rng)*1e-12) => "AUCT"
+        );
+        contrasts = Dict(
+            [
+                :trt,
+                :per,
+                :sub,
+                :row,
+            ] .=> Ref(DummyCoding())
+        )
+    )
+    m.optsum.backend = :prima
+    # m.optsum.optimizer = :cobyla
+    m.optsum.optimizer = :bobyqa
+    fit!(
+        m;
+        REML = true
+    )
+end
+
+countmap(round.(exp.(getindex.(coef.(fts), 2)), digits=3))

issues/705/saddlepointdata.csv

@@ -0,0 +1,230 @@
+sub,seq,per,trt,AUCT
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issues/833/833.jl

@@ -0,0 +1,17 @@
+using DataFrames, CSV, MixedModels, CairoMakie
+
+df = CSV.read("issues/833/data.csv", DataFrame)
+
+_lmm = LinearMixedModel(
+    @formula(endpoint ~ 1 + formulation + sequence + period + (1 | id)),
+    df;
+    contrasts = Dict(:period => DummyCoding()),
+)
+
+_lmm.optsum.optimizer = :LN_COBYLA
+
+fit!(_lmm; REML = true, fitlog=true)
+
+θ = copy(_lmm.θ)                      # keep a copy of the optimal θ
+lines(0.4:0.01:1.0, objective!(_lmm)) # I wrote the method for objective! then forgot it 
+objective!(_lmm, θ)