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PharmCat · Jan 9, 2021 · 8bd70e6 · 8bd70e6
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Showing 10 changed files with 74 additions and 39 deletions.
diff --git a/.gitignore b/.gitignore
@@ -1,5 +1,5 @@
 src/temp.jl
-test/precompile/
+#test/precompile/
 test/devtest.jl
 examples/
 docs/build/

diff --git a/src/gmat.jl b/src/gmat.jl
@@ -28,7 +28,7 @@ function gmat_switch!(G, θ, covstr, i)
     elseif covstr.random[i].covtype.s == :ZERO
         gmat_zero!(G, similar(θ, 0), covstr.q[i], covstr.random[i].covtype)
     else
-        throw(ErrorException("Unknown covariance structure: $(covstr.random[i].covtype.s), n = $(i)"))
+        error("Unknown covariance structure!")
     end
     G
 end

diff --git a/src/rmat.jl b/src/rmat.jl
@@ -3,7 +3,7 @@
 ################################################################################
 
 ################################################################################
-function rmat_basep!(mx, θ::AbstractVector{T}, zrv, covstr) where T
+function rmat_basep!(mx, θ::AbstractVector{T}, zrv, covstr::CovStructure{T2}) where T where T2
         if covstr.repeated.covtype.s == :SI
             rmatp_si!(mx, θ, zrv, covstr.repeated.covtype)
         elseif covstr.repeated.covtype.s == :DIAG
@@ -17,7 +17,7 @@ function rmat_basep!(mx, θ::AbstractVector{T}, zrv, covstr) where T
         elseif covstr.repeated.covtype.s == :CS
             rmatp_cs!(mx, θ, zrv, covstr.repeated.covtype)
         else
-            throw(ErrorException("Unknown covariance structure: $(covstr.repeated.covtype.s)"))
+            error("Unknown covariance structure!")
         end
 end
 ################################################################################
@@ -36,7 +36,7 @@ function rmat_basep_z!(mx, θ::AbstractVector{T}, zrv, covstr) where T
         elseif covstr.repeated.covtype.s == :CS
             rmatp_cs!(view(mx, covstr.block[end][i], covstr.block[end][i]), θ, view(covstr.rz, covstr.block[end][i], :), covstr.repeated.covtype)
         else
-            throw(ErrorException("Unknown covariance structure: $(covstr.repeated.covtype.s)"))
+            error("Unknown covariance structure!")
         end
     end
     mx

diff --git a/test/ar.jl b/test/ar.jl
@@ -5,23 +5,24 @@ REML 453.339544
 =#
 df        = CSV.File(path*"/csv/RepeatedPulse.csv") |> DataFrame
 df.Pulse = float.(df.Pulse)
-categorical!(df, :Time);
-categorical!(df, :Day);
+transform!(df, :Time => categorical, renamecols=false)
+transform!(df, :Day => categorical, renamecols=false)
+
 @testset "  RepeatedPulse.csv" begin
     lmm = Metida.LMM(@formula(Pulse~1), df;
     random = Metida.VarEffect(Metida.@covstr(Time), Metida.SI),
     repeated = Metida.VarEffect(Metida.@covstr(Day), Metida.AR),
     subject = :Time
     )
     Metida.fit!(lmm)
-    @test lmm.result.reml ≈ 450.75260020816347 atol=1E-6
+    @test lmm.result.reml ≈ 453.1926914954099 atol=1E-6
 
     lmm = Metida.LMM(@formula(Pulse~1), df;
     repeated = Metida.VarEffect(Metida.@covstr(1), Metida.AR),
     subject = :Time
     )
     Metida.fit!(lmm)
-    @test lmm.result.reml ≈ 462.66964037860635 atol=1E-6
+    @test lmm.result.reml ≈ 470.5275730199629 atol=1E-6
 end
 
 #=
@@ -32,17 +33,17 @@ df        = CSV.File(path*"/csv/ChickWeight.csv") |> DataFrame
 df.weight = float.(df.weight)
 df.Time = float.(df.Time)
 df.Time2 = copy(df.Time)
-categorical!(df, :Time2);
-categorical!(df, :Chick);
-categorical!(df, :Diet);
+transform!(df, :Time2 => categorical, renamecols=false)
+transform!(df, :Chick => categorical, renamecols=false)
+transform!(df, :Diet => categorical, renamecols=false)
 @testset "  ChickWeight.csv" begin
     lmm = Metida.LMM(@formula(weight~1 + Diet & Time), df;
     random = Metida.VarEffect(Metida.@covstr(1), Metida.SI),
     repeated = Metida.VarEffect(Metida.@covstr(Diet), Metida.ARH),
     subject = :Chick
     )
     Metida.fit!(lmm)
-    @test lmm.result.reml ≈ 5453.137479692881 atol=1E-6
+    @test lmm.result.reml ≈ 5451.857613990478 atol=1E-6
 
     lmm = Metida.LMM(@formula(weight~1 + Diet & Time), df;
     random = Metida.VarEffect(Metida.@covstr(Diet), Metida.ARH),

diff --git a/test/berds.jl b/test/berds.jl
@@ -29,10 +29,11 @@ df        = CSV.File(path*"/csv/berds/rds29.csv", types = Dict(:PK => Float64))
 for i = 1:30
     df        = CSV.File(path*"/csv/berds/rds"*string(i)*".csv", types = Dict(:PK => Float64)) |> DataFrame
     dropmissing!(df)
-    categorical!(df, :subject);
-    categorical!(df, :period);
-    categorical!(df, :sequence);
-    categorical!(df, :treatment);
+    transform!(df, :subject => categorical, renamecols=false)
+    transform!(df, :period => categorical, renamecols=false)
+    transform!(df, :sequence => categorical, renamecols=false)
+    transform!(df, :treatment => categorical, renamecols=false)
+
     df.lnpk = log.(df.PK)
 
     @testset "  Test $(i)" begin

diff --git a/test/lme4.jl b/test/lme4.jl
@@ -1,8 +1,9 @@
 
 
 df        = CSV.File(path*"/csv/lme4/sleepstudy.csv") |> DataFrame
-categorical!(df, :Subject);
-categorical!(df, :Days);
+transform!(df, :Subject => categorical, renamecols=false)
+transform!(df, :Days => categorical, renamecols=false)
+
 
 #=
 SPSS
@@ -19,8 +20,9 @@ end
 
 df        = CSV.File(path*"/csv/lme4/Penicillin.csv") |> DataFrame
 df.diameter = float.(df.diameter)
-categorical!(df, :plate);
-categorical!(df, :sample);
+transform!(df, :plate => categorical, renamecols=false)
+transform!(df, :sample => categorical, renamecols=false)
+
 
 @testset " Penicillin.csv" begin
     lmm = Metida.LMM(@formula(diameter~1), df;
@@ -32,9 +34,10 @@ end
 
 
 df        = CSV.File(path*"/csv/lme4/Pastes.csv") |> DataFrame
-categorical!(df, :batch);
-categorical!(df, :cask);
-categorical!(df, :sample);
+transform!(df, :batch => categorical, renamecols=false)
+transform!(df, :sample => categorical, renamecols=false)
+transform!(df, :cask=> categorical, renamecols=false)
+
 @testset " Pastes.csv" begin
     lmm = Metida.LMM(@formula(strength~1), df;
     random = [Metida.VarEffect(Metida.SI, subj = :batch), Metida.VarEffect(Metida.SI, subj = [:batch,  :cask])]

diff --git a/test/precompile/metida_compiles.log b/test/precompile/metida_compiles.log
diff --git a/test/snoopy.jl b/test/snoopy.jl
@@ -3,7 +3,7 @@ using SnoopCompile
 path    = dirname(@__FILE__)
 cd(path)
 
-inf_timing = @snoopi tmin=0.0001 include("test.jl")
+inf_timing = @snoopi tmin=0.000001 include("test.jl")
 pc = SnoopCompile.parcel(inf_timing)
 SnoopCompile.write("precompile", pc)
 

diff --git a/test/testdata.jl b/test/testdata.jl
@@ -2,7 +2,7 @@
 
 #Simple dataset
 df0         = CSV.File(path*"/csv/df0.csv") |> DataFrame
-categorical!(df0, :subject);
-categorical!(df0, :period);
-categorical!(df0, :sequence);
-categorical!(df0, :formulation);
+transform!(df0, :subject => categorical, renamecols=false)
+transform!(df0, :period => categorical, renamecols=false)
+transform!(df0, :sequence => categorical, renamecols=false)
+transform!(df0, :formulation=> categorical, renamecols=false)
diff --git a/test/warn.jl b/test/warn.jl
@@ -1,17 +1,47 @@
+#Code warntype
 
+@code_warntype Metida.reml_sweep_β3(lmm, lmm.result.theta)
 
-lmm = Metida.LMM(@formula(var~sequence+period+formulation), df;
-random = [Metida.VarEffect(Metida.@covstr(formulation), Metida.CSH)],
-repeated = Metida.VarEffect(Metida.@covstr(formulation), Metida.VC),
-subject = :subject)
+@code_typed Metida.reml_sweep_β3(lmm, lmm.result.theta)
 
-lmmr = Metida.fit!(lmm)
+@code_warntype  Metida.gmat_base(lmm.result.theta, lmm.covstr)
 
-@code_warntype Metida.reml_sweep_β(lmm, lmm.result.theta)
+@code_typed  Metida.gmat_base(lmm.result.theta, lmm.covstr)
 
-@code_typed Metida.reml_sweep_β(lmm, lmm.result.theta)
+V = [1. 2 3 4;
+1 2 3 3;
+9 8 2 1;
+1 2 1 2]
 
-V = [1 2 3 4; 1 2 3 3; 9 8 2 1; 1 2 1 2]
-@code_warntype Metida.rmat_basep!(V, lmm.result.theta[lmm.covstr.tr[end]], lmm.data.zrv[1], lmm.covstr)
+@code_warntype Metida.rmat_basep!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.data.block[1],:), lmm.covstr)
 
-@code_warntype Metida.gmat_base(lmm.result.theta, lmm.covstr)
+@code_warntype Metida.gmat_base_z2!(V, lmm.result.theta, lmm.covstr, lmm.data.block[1], lmm.covstr.sblock[1])
+
+@code_warntype Metida.rmat_basep_z2!(V, lmm.result.theta[lmm.covstr.tr[end]], lmm.covstr, lmm.data.block[1], lmm.covstr.sblock[1])
+
+@code_warntype Metida.rmatp_si!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_typed Metida.rmatp_si!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_warntype Metida.rmatp_diag!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_warntype Metida.rmatp_csh!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_warntype Metida.rmatp_cs!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_warntype Metida.rmatp_ar!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+@code_warntype Metida.rmatp_arh!(V, lmm.result.theta[lmm.covstr.tr[end]], view(lmm.covstr.rz, lmm.covstr.block[end][1], :), lmm.covstr.repeated.covtype)
+
+
+G = [1. 2.;
+2. 3.]
+@code_warntype Metida.gmat_si!(G, lmm.result.theta[lmm.covstr.tr[1]], lmm.covstr.q[1], lmm.covstr.random[1].covtype)
+
+@code_warntype Metida.gmat_diag!(G, lmm.result.theta[lmm.covstr.tr[1]], lmm.covstr.q[1], lmm.covstr.random[1].covtype)
+
+@code_warntype Metida.gmat_csh!(G, lmm.result.theta[lmm.covstr.tr[1]], lmm.covstr.q[1], lmm.covstr.random[1].covtype)
+
+@code_warntype Metida.gmat_cs!(G, lmm.result.theta[lmm.covstr.tr[1]], lmm.covstr.q[1], lmm.covstr.random[1].covtype)
+
+@code_warntype Metida.gmat_ar!(G, lmm.result.theta[lmm.covstr.tr[1]], lmm.covstr.q[1], lmm.covstr.random[1].covtype)