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Add data and script for issue 833 (#835)
* Add data and script for issue 833 * Use objective! to evaluate objective * Add issues/705 directory
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‎issues/705/705.jl

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

‎issues/705/saddlepointdata.csv

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sub,seq,per,trt,AUCT
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64,TRTR,4,R,94.14905294402817

‎issues/833/833.jl

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

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