fix effect modef with constant , 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 = "0.16.2"
+version = "0.16.3"
 
 [deps]
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"

docs/src/examples.md

@@ -1,10 +1,12 @@
-### Example 1 - Continuous and categorical predictors
+## Example 1 - Continuous and categorical predictors
 
 ```@example lmmexample
 using Metida, CSV, DataFrames, CategoricalArrays, MixedModels;
 
 rds = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "1fptime.csv"); types = [String, String, Float64, Float64]) |> DataFrame
 
+rds2 = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "ftdf3.csv"); types = [String,  Float64, Float64, String, String, String, String, String, Float64]) |> DataFrame
+
 nothing; # hide
 ```
 
@@ -27,7 +29,7 @@ mm = fit(MixedModel, fm, rds, REML=true)
 println(mm) #hide
 ```
 
-### Example 2 - Two random factors (Penicillin data)
+## Example 2 - Two random factors (Penicillin data)
 
 Metida:
 
@@ -51,7 +53,7 @@ mm = fit(MixedModel, fm2, df, REML=true)
 println(mm) #hide
 ```
 
-### Example 3 - Repeated ARMA/AR/ARH
+## Example 3 - Repeated ARMA/AR/ARH
 
 ```@example lmmexample
 rds = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "1freparma.csv"); types = [String, String, Float64, Float64]) |> DataFrame
@@ -91,9 +93,9 @@ repeated = VarEffect(@covstr(1|subject&factor), ARH),
 fit!(lmm)
 ```
 
-### Example 4 - SAS relation
+## Example 4 - SAS relation
 
-#### Model 1
+### Model 1
 
 ```
 df0 = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv", "df0.csv")) |> DataFrame
@@ -116,7 +118,7 @@ REPEATED/GRP=formulation SUB=subject R;
 RUN;
 ```
 
-#### Model 2
+### Model 2
 
 ```
 lmm = LMM(
@@ -138,7 +140,7 @@ REPEATED/GRP=formulation SUB=subject R;
 RUN;
 ```
 
-#### Model 3
+### Model 3
 
 ```
 lmm = LMM(@formula(var ~ sequence + period + formulation), df0;
@@ -157,7 +159,7 @@ RANDOM  subject/TYPE=VC G V;
 RUN;
 ```
 
-### Example 5 - Working with Effects.jl
+## Example 5 - Working with Effects.jl
 
 ```
 using Effects, StatsModels
@@ -172,4 +174,26 @@ table_model = StatsModels.TableRegressionModel(lmm, lmm.mf, lmm.mm)
 emmeans(tm)
 
 effects(Dict(:period => ["1", "2", "3", "4"]), tm)
-```
+```
+
+
+## Unstructured covariance 
+
+Unstructured covariance example.
+
+
+Metida result:
+
+```@example lmmexample
+lmm = Metida.LMM(@formula(response~factor), ftdf3;
+    random = Metida.VarEffect(Metida.@covstr(r1|subject), UN),
+    )
+Metida.fit!(lmm)
+```
+
+MixedModels result:
+
+```@example lmmexample
+mm = fit(MixedModel, @formula(response ~ factor+ (0+r1|subject)), ftdf3, REML = true)
+println(mm) #hide
+```

src/varstruct.jl

@@ -31,7 +31,7 @@ Macros for random/repeated effect model.
 # Example
 
 ```julia
-@covstr(factor|subject)
+@covstr(model|subject)
 ```
 """
 macro covstr(ex)
@@ -64,7 +64,7 @@ Random/repeated effect.
 
 !!! note
 
-    Categorical factors are coded with `FullDummyCoding()` by default, use `coding` for other contrast codeing.
+    Categorical factors are coded with `FullDummyCoding()` by default, use `coding` for other contrast coding.
 
 # Example
 
@@ -435,7 +435,15 @@ end
 #                            CONTRAST CODING
 ################################################################################
 
-function fill_coding_dict!(t::T, d::Dict, data) where T <: Union{ConstantTerm, InterceptTerm, FunctionTerm}
+function fill_coding_dict!(t::T, d::Dict, data) where T <: Union{ConstantTerm, InterceptTerm}
+    return d
+end
+function fill_coding_dict!(t::T, d::Dict, data) where T <: FunctionTerm
+    if t.f === +
+        for i in t.args
+            fill_coding_dict!(i, d, data)
+        end
+    end
     return d
 end
 function fill_coding_dict!(t::T, d::Dict, data) where T <: Term

test/test.jl

@@ -289,6 +289,18 @@ include("testdata.jl")
     fit!(lmm)
     @test_nowarn show(io, lmm)
 
+    # Intercept term in random part
+    lmm = Metida.LMM(@formula(var~1), df0;
+    random = Metida.VarEffect(Metida.@covstr(1+formulation|subject)))
+    @test typeof( lmm.covstr.random[1].coding[:formulation]) <: StatsModels.FullDummyCoding
+    # Zero term in random part
+    lmm = Metida.LMM(@formula(var~1), df0;
+    random = Metida.VarEffect(Metida.@covstr(0+formulation|subject)))
+    @test typeof( lmm.covstr.random[1].coding[:formulation]) <: StatsModels.FullDummyCoding
+    # Intercept term in random part and coding
+    lmm = Metida.LMM(@formula(var~1), df0;
+    random = Metida.VarEffect(Metida.@covstr(1+formulation|subject), coding = Dict(:formulation => StatsModels.DummyCoding())))
+    @test typeof( lmm.covstr.random[1].coding[:formulation]) <: StatsModels.DummyCoding
 end
 ################################################################################
 #                                  df0