allow predicting from a single observation

NEWS.md

@@ -1,3 +1,7 @@
+MixedModels v4.8.0 Release Notes
+==============================
+* Allow predicting from a single observation, as long as `Grouping()` is used for the grouping variables. The simplified implementation of `Grouping()` also removes several now unnecessary `StatsModels` methods that should not have been called directly by the user. [#653]
+
 MixedModels v4.7.3 Release Notes
 ==============================
 * More informative error message for formulae lacking random effects [#651]
@@ -375,3 +379,4 @@ Package dependencies
 [#637]: https://github.com/JuliaStats/MixedModels.jl/issues/637
 [#648]: https://github.com/JuliaStats/MixedModels.jl/issues/648
 [#651]: https://github.com/JuliaStats/MixedModels.jl/issues/651
+[#653]: https://github.com/JuliaStats/MixedModels.jl/issues/653

Project.toml

@@ -1,7 +1,7 @@
 name = "MixedModels"
 uuid = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
 author = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>", "Jose Bayoan Santiago Calderon <[email protected]>"]
-version = "4.7.3"
+version = "4.8.0"
 
 [deps]
 Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"

src/grouping.jl

@@ -21,14 +21,16 @@ julia> schema((; grp = string.(1:100_000)), Dict(:grp => Grouping()))
 """
 struct Grouping <: StatsModels.AbstractContrasts end
 
-# return an empty matrix
-StatsModels.contrasts_matrix(::Grouping, baseind, n) = zeros(0, 0)
-StatsModels.termnames(::Grouping, levels::AbstractVector, baseind::Integer) = levels
-
 # this is needed until StatsModels stops assuming all contrasts have a .levels field
 Base.getproperty(g::Grouping, prop::Symbol) = prop == :levels ? nothing : getfield(g, prop)
 
 # special-case categorical terms with Grouping contrasts.
 function StatsModels.modelcols(::CategoricalTerm{Grouping}, d::NamedTuple)
     return error("can't create model columns directly from a Grouping term")
 end
+
+function StatsModels.ContrastsMatrix(
+    contrasts::Grouping, levels::AbstractVector
+)
+    return StatsModels.ContrastsMatrix(zeros(0, 0), levels, levels, contrasts)
+end

test/predict.jl

@@ -47,6 +47,13 @@ include("modelcache.jl")
 end
 
 @testset "predict" begin
+
+    @testset "single obs" begin
+        kb07 = DataFrame(dataset(:kb07))
+        m = models(:kb07)[1]
+        only(predict(m, kb07[1:1, :])) ≈ first(fitted(m))
+    end
+
     slp = DataFrame(dataset(:sleepstudy))
     slp2 = transform(slp, :subj => ByRow(x -> (x == "S308" ? "NEW" : x)) => :subj)
     slpm = allowmissing(slp, :reaction)