added lmm and glmm as convenience wrappers for fit

src/MixedModels.jl

@@ -110,6 +110,7 @@ export @formula,
     issingular,
     leverage,
     levels,
+    lmm,
     logdet,
     loglikelihood,
     lowerbd,

src/linearmixedmodel.jl

@@ -213,10 +213,21 @@ function StatsAPI.fit(::Type{LinearMixedModel},
     σ=nothing,
     amalgamate=true,
     kwargs...)
-    lmm = LinearMixedModel(f, tbl; contrasts, wts, σ, amalgamate)
-    return fit!(lmm; kwargs...)
+    lmod = LinearMixedModel(f, tbl; contrasts, wts, σ, amalgamate)
+    return fit!(lmod; kwargs...)
 end
 
+"""
+    lmm(f::FormulaTerm, tbl; kwargs...)
+    lmm(
+        f::FormulaTerm, tbl;
+        wts=[], contrasts=Dict{Symbol,Any}(), σ=nothing, amalgamate=true,
+        kwargs...)
+
+    Aliases for the fit(LinearMixedModel, ...) functions
+"""
+lmm(args...; kwargs...) = fit(LinearMixedModel, args...; kwargs...)
+
 function _offseterr()
     return throw(
         ArgumentError(

src/predict.jl

@@ -119,11 +119,11 @@ function _predict(m::MixedModel{T}, newdata, β; new_re_levels) where {T}
                 ),
             )
         end
-        lmm = LinearMixedModel(f, newdata; contrasts=contr)
+        lmod = LinearMixedModel(f, newdata; contrasts=contr)
         ytemp =
             new_re_levels == :missing ? convert(Vector{Union{T,Missing}}, ytemp) : ytemp
 
-        ytemp, lmm
+        ytemp, lmod
     end
 
     pivotmatch = pivot(mnew)[pivot(m)]

test/fit.jl

@@ -2,9 +2,28 @@ using MixedModels
 using Suppressor
 using Test
 
-@testset "linear" begin
+@testset "linear, and lmm wrapper" begin
     m1 = fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff); progress=false)
     @test first(m1.θ) ≈ 0.7525806757718846 rtol=1.0e-5
+    m2 = lmm(@formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff); progress=false)
+    @test isa(m2, LinearMixedModel)
+    @test first(m2.θ) ≈ 0.7525806757718846 rtol=1.0e-5
+    @test deviance(m1) ≈ deviance(m2) #TODO: maybe add an `rtol`?
+    @test isa(lmm(@formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff); progress=false, REML = true), LinearMixedModel)
+
+    # example from https://github.com/JuliaStats/MixedModels.jl/issues/194
+    # copied from tetst/pls.jl
+    data = (
+        a = [1.55945122,0.004391538,0.005554163,-0.173029772,4.586284429,0.259493671,-0.091735715,5.546487603,0.457734831,-0.030169602],
+        b = [0.24520519,0.080624178,0.228083467,0.2471453,0.398994279,0.037213859,0.102144973,0.241380251,0.206570975,0.15980803],
+        c = PooledArray(["H","F","K","P","P","P","D","M","I","D"]),
+        w1 = [20,40,35,12,29,25,65,105,30,75],
+        w2 = [0.04587156,0.091743119,0.080275229,0.027522936,0.066513761,0.05733945,0.149082569,0.240825688,0.068807339,0.172018349],
+    )
+    m2 = lmm(@formula(a ~ 1 + b + (1|c)), data; wts = data.w1, progress=false)
+    @test m2.θ ≈ [0.295181729258352]  atol = 1.e-4
+    @test stderror(m2) ≈  [0.9640167, 3.6309696] atol = 1.e-4
+    @test vcov(m2) ≈ [0.9293282 -2.557527; -2.5575267 13.183940] atol = 1.e-4
 end
 
 @testset "generalized" begin