Fix bug with predict(mm::TableRegressionModel, data; kwargs...) (#126)

Makes `predict` work even when the dependent (left-hand side) variables are not in the new data table.

* Fix bug with predict(mm::TableRegressionModel, data; kwargs...)

* Bump version number from "0.6.0" to "0.6.1"

Project.toml

@@ -1,6 +1,6 @@
 name = "StatsModels"
 uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
-version = "0.6.0"
+version = "0.6.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

src/statsmodel.jl

@@ -159,7 +159,7 @@ function StatsBase.predict(mm::TableRegressionModel, data; kwargs...)
         throw(ArgumentError("expected data in a Table, got $(typeof(data))"))
 
     f = mm.mf.f
-    cols, nonmissings = missing_omit(columntable(data), f)
+    cols, nonmissings = missing_omit(columntable(data), f.rhs)
     new_x = modelcols(f.rhs, cols)
     y_pred = predict(mm.model, reshape(new_x, size(new_x, 1), :);
                      kwargs...)

test/statsmodel.jl

@@ -110,6 +110,14 @@ Base.show(io::IO, m::DummyModTwo) = println(io, m.msg)
     @test_throws KeyError predict(m2, d3)
     # @test_throws ArgumentError predict(m2, d3)
 
+    ## predict with dataframe that doesn't have the dependent variable
+    d4 = deepcopy(d)
+    deletecols!(d4, [:y])
+    @test predict(m, d4) == predict(m, d)
+
+    ## attempting to fit with d4 should fail since it doesn't have :y
+    @test_throws ErrorException fit(DummyMod, f, d4)
+
     ## fit with contrasts specified
     d[:x2p] = CategoricalVector{Union{Missing, Int}}(d[:x2])
     f3 = @formula(y ~ x1p + x2p)