m2logml, logml, docs

Project.toml

@@ -3,7 +3,7 @@ uuid = "a1dec852-9fe5-11e9-361f-8d9fde67cfa2"
 keywords = ["lenearmodel", "mixedmodel"]
 desc = "Mixed-effects models with flexible covariance structure."
 authors = ["Vladimir Arnautov <[email protected]>"]
-version = "0.16.1"
+version = "0.16.2"
 
 [deps]
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"

docs/src/api.md

@@ -230,10 +230,28 @@ Metida.thetalength
 
 ### Metida.vmatrix
 
+```@docs
+Metida.vmatrix
+```
+
+### Metida.vmatrix!
+
 ```@docs
 Metida.vmatrix!
 ```
 
+### Metida.m2logreml
+
+```@docs
+Metida.m2logreml
+```
+
+### Metida.logreml
+
+```@docs
+Metida.logreml
+```
+
 ## StatsAPI
 
 ### Metida.aic
@@ -463,3 +481,8 @@ Metida.raneflenv
 Metida.edistance  
 ```
 
+### Metida.edistance  
+
+```@docs
+Metida.edistance  
+```

src/Metida.jl

@@ -36,7 +36,7 @@ SPPOW, SpatialPower,
 SPGAU, SpatialGaussian, RawCoding,
 UN, Unstructured,
 CovarianceType,
-fit, fit!, LMM, VarEffect, theta, logreml, m2logreml, thetalength, dof_satter, dof_contain, rankx, caic, lcontrast, typeiii, estimate, contrast,
+fit, fit!, LMM, VarEffect, theta, logreml, m2logreml, m2logml, logml, thetalength, dof_satter, dof_contain, rankx, caic, lcontrast, typeiii, estimate, contrast,
 gmatrix, rmatrix, vmatrix!, responsename, nblocks, raneff,
 AbstractCovarianceType, AbstractCovmatMethod, MetidaModel,
 getlog, rand, rand!,

src/utils.jl

@@ -195,15 +195,50 @@ function varlinkvecapply(v, p; varlinkf = :exp, rholinkf = :sigm)
     return s
 end
 ################################################################################
-function m2logreml(lmm)
-    return lmm.result.reml
+
+"""
+    m2logreml(lmm::LMM, θ = theta(lmm); maxthreads::Int = num_cores())
+
+-2 logREML
+"""
+function m2logreml(lmm::LMM, θ = theta(lmm); maxthreads::Int = num_cores())
+    if θ == theta(lmm)
+        return lmm.result.reml
+    else
+        return reml_sweep_β(lmm, LMMDataViews(lmm), θ; maxthreads = maxthreads)[1]
+    end
+end
+"""
+    logreml(lmm::LMM, θ = theta(lmm); maxthreads::Int = num_cores())
+
+logREML
+"""
+function logreml(lmm::LMM, θ = theta(lmm); maxthreads::Int = num_cores())
+    return -m2logreml(lmm, θ; maxthreads = maxthreads)/2
 end
-function logreml(lmm)
-    return -m2logreml(lmm)/2
+
+
+"""
+    m2logml(lmm::LMM, β = coef(lmm), θ = theta(lmm); maxthreads::Int = num_cores())
+
+-2 logML
+"""
+function m2logml(lmm::LMM, β = coef(lmm), θ = theta(lmm); maxthreads::Int = num_cores())
+    c             = length(lmm.data.yv)*log(2π)
+    θ₁, θ₂, θ₃, noerror = core_sweep_β(lmm, LMMDataViews(lmm), θ, β, length(lmm.covstr.vcovblock); maxthreads = maxthreads)
+    if !noerror @warn "There was probably an error during the calculations. The result may be incorrect." end
+    return   θ₁ + θ₃ + c
 end
-function m2logreml(lmm, theta; maxthreads::Int = num_cores())
-    return reml_sweep_β(lmm, LMMDataViews(lmm), theta; maxthreads = maxthreads)[1]
+"""
+    logml(lmm::LMM, beta = coef(lmm), θ = theta(lmm); maxthreads::Int = num_cores())
+
+logML
+"""
+function logml(lmm::LMM, beta = coef(lmm), θ = theta(lmm); maxthreads::Int = num_cores())
+    return -m2logml(lmm, beta, θ; maxthreads = maxthreads)/2
 end
+
+
 ################################################################################
 
 function optim_callback(os)

test/test.jl

@@ -51,7 +51,44 @@ include("testdata.jl")
     random = Metida.VarEffect(Metida.@covstr(formulation|subject), Metida.DIAG),
     )
     @test Metida.m2logreml(lmm) ≈ 16.241112644506067 atol=1E-6
+    @test Metida.logreml(lmm) ≈ -0.5*16.241112644506067 atol=1E-6
 
+    @test Metida.m2logreml(lmm, [0.447322, 0.367367, 0.185068]) ≈ 16.241112644506067 atol=1E-6
+    @test Metida.m2logreml(lmm, [0.5, 0.3, 0.2]) ≈ 16.5746217198294 atol=1E-6
+
+    # core_sweep_β REML, ML estimate test; 
+    reml, θs₂, remlθ₃, noerror = Metida.reml_sweep_β(lmm, Metida.LMMDataViews(lmm), Metida.theta(lmm), Metida.coef(lmm))
+    @test reml ≈ 16.241112644506067 atol=1E-6
+    @test θs₂ ≈ [55.8946  33.5368    13.4807   14.4666    13.4807   32.8767
+    33.5368  33.5368     6.90537   9.86301    6.90537  19.726
+    13.4807   6.90537   79.7331    0.0      -66.2523    6.57534
+    14.4666   9.86301    0.0      80.226      0.0       9.86301
+    13.4807   6.90537  -66.2523    0.0       79.7331    6.57534
+    32.8767  19.726      6.57534   9.86301    6.57534  32.8767] atol=1E-3
+    @test remlθ₃ ≈ 13.999999919958947 atol=1E-6
+
+    θ₁, θ₂, θ₃, noerror = Metida.core_sweep_β(lmm, Metida.LMMDataViews(lmm), Metida.theta(lmm), Metida.coef(lmm), length(lmm.covstr.vcovblock))
+    @test noerror == true
+    c             = (length(lmm.data.yv) - lmm.rankx)*log(2π)
+    @test θ₁ ≈  -43.860020472151916 atol=1E-6
+    @test θ₂ ≈ [55.8946  33.5368  13.4807   14.4666    13.4807   32.8767
+    0.0     33.5368   6.90537   9.86301    6.90537  19.726
+    0.0      0.0     79.7331    0.0      -66.2523    6.57534
+    0.0      0.0      0.0      80.226      0.0       9.86301
+    0.0      0.0      0.0       0.0       79.7331    6.57534
+    0.0      0.0      0.0       0.0        0.0      32.8767] atol=1E-3
+    @test θ₃ ≈ 13.999999919958947 atol=1E-6
+    logdetθ₂ = logdet(Symmetric(θ₂))
+    @test logdetθ₂  ≈ 20.370854266968205 atol=1E-6
+    @test θ₁ + logdetθ₂ + θ₃ + c ≈ 16.241112644506067 atol=1E-6
+    # ML test
+    c = length(lmm.data.yv)*log(2π)
+    @test θ₁ + θ₃ + c ≈ 6.897520775993932 atol=1E-6
+    @test  Metida.m2logml(lmm, coef(lmm), Metida.theta(lmm); maxthreads = 8) ≈ 6.897520775993932
+    @test  Metida.m2logml(lmm, coef(lmm)) ≈ 6.897520775993932
+    @test  Metida.m2logml(lmm) ≈ 6.897520775993932
+    @test  Metida.logml(lmm) ≈ -0.5*6.897520775993932
+    #
     lmm = Metida.fit(Metida.LMM, Metida.@lmmformula(var~0+sequence+period+formulation,
     random = formulation|subject:Metida.DIAG), df0)
     @test Metida.fixedeffn(lmm) == 3