[WIP] Handle missing values

src/contrasts.jl

@@ -207,8 +207,13 @@ function termnames(C::AbstractContrasts, levels::AbstractVector, baseind::Intege
     levels[not_base]
 end
 
-Base.getindex(contrasts::ContrastsMatrix{C,T}, rowinds, colinds) where {C,T} =
-    getindex(contrasts.matrix, getindex.(Ref(contrasts.invindex), rowinds), colinds)
+function Base.getindex(contrasts::ContrastsMatrix{C,T}, rowinds, colinds) where {C,T}
+    # allow rows to be missing
+    rows = get.(Ref(contrasts.invindex), rowinds, missing)
+    # create a row of nothing but missings for missing values
+    mrow = reduce(vcat, [missing for c in getindex(contrasts.matrix, 1, colinds)])
+    vcat([r === missing ? mrow : getindex(contrasts.matrix, r, colinds) for r in rows])
+end
 
 # Making a contrast type T only requires that there be a method for
 # contrasts_matrix(T,  baseind, n) and optionally termnames(T, levels, baseind)

src/modelframe.jl

@@ -79,11 +79,11 @@ missing_omit(data::T, formula::AbstractTerm) where T<:ColumnTable =
 
 function ModelFrame(f::FormulaTerm, data::ColumnTable;
                     model::Type{M}=StatisticalModel, contrasts=Dict{Symbol,Any}()) where M
-    data, _ = missing_omit(data, f)
-
     sch = schema(f, data, contrasts)
     f = apply_schema(f, sch, M)
-
+
+    data, _ = missing_omit(data, f)
+
     ModelFrame(f, sch, data, model)
 end
 

src/schema.jl

@@ -60,7 +60,7 @@ Base.haskey(schema::Schema, key) = haskey(schema.schema, key)
 Compute all the invariants necessary to fit a model with `terms`.  A schema is a dict that
 maps `Term`s to their concrete instantiations (either `CategoricalTerm`s or
 `ContinuousTerm`s.  "Hints" may optionally be supplied in the form of a `Dict` mapping term
-names (as `Symbol`s) to term or contrast types.  If a hint is not provided for a variable, 
+names (as `Symbol`s) to term or contrast types.  If a hint is not provided for a variable,
 the appropriate term type will be guessed based on the data type from the data column: any
 numeric data is assumed to be continuous, and any non-numeric data is assumed to be
 categorical.
@@ -91,7 +91,7 @@ StatsModels.Schema with 1 entry:
   y => y
 ```
 
-Note that concrete `ContinuousTerm` and `CategoricalTerm` and un-typed `Term`s print the 
+Note that concrete `ContinuousTerm` and `CategoricalTerm` and un-typed `Term`s print the
 same in a container, but when printed alone are different:
 
 ```jldoctest 1
@@ -176,6 +176,8 @@ concrete_term(t::Term, d) = concrete_term(t, d, nothing)
 concrete_term(t, d, hint) = t
 
 concrete_term(t::Term, xs::AbstractVector{<:Number}, ::Nothing) = concrete_term(t, xs, ContinuousTerm)
+# and for missing values
+concrete_term(t::Term, xs::AbstractVector{Union{Missing,T}} where T<:Number, ::Nothing) = concrete_term(t, xs, ContinuousTerm)
 function concrete_term(t::Term, xs::AbstractVector, ::Type{ContinuousTerm})
     μ, σ2 = StatsBase.mean_and_var(xs)
     min, max = extrema(xs)
@@ -196,9 +198,9 @@ end
 Return a new term that is the result of applying `schema` to term `t` with
 destination model (type) `Mod`.  If `Mod` is omitted, `Nothing` will be used.
 
-When `t` is a `ContinuousTerm` or `CategoricalTerm` already, the term will be returned 
-unchanged _unless_ a matching term is found in the schema.  This allows 
-selective re-setting of a schema to change the contrast coding or levels of a 
+When `t` is a `ContinuousTerm` or `CategoricalTerm` already, the term will be returned
+unchanged _unless_ a matching term is found in the schema.  This allows
+selective re-setting of a schema to change the contrast coding or levels of a
 categorical term, or to change a continuous term to categorical or vice versa.
 
 When defining behavior for custom term types, it's best to dispatch on
@@ -277,7 +279,7 @@ function apply_schema(t::FormulaTerm, schema::Schema, Mod::Type{<:StatisticalMod
 end
 
 # strategy is: apply schema, then "repair" if necessary (promote to full rank
-# contrasts).  
+# contrasts).
 #
 # to know whether to repair, need to know context a term appears in.  main
 # effects occur in "own" context.

src/terms.jl

@@ -483,6 +483,13 @@ modelcols(t::Term, d::NamedTuple) =
 modelcols(ft::FunctionTerm{Fo,Fa,Names}, d::NamedTuple) where {Fo,Fa,Names} =
     ft.fanon.(getfield.(Ref(d), Names)...)
 
+# this is weird, but using import Base: copy leads to exporting type piracy
+# for non missing values, the compiler should hopefully optimize down the extra
+# layer of indirection
+function copy end
+copy(x::Any) = Base.copy(x)
+copy(m::Missing) = m
+
 modelcols(t::ContinuousTerm, d::NamedTuple) = copy.(d[t.sym])
 
 modelcols(t::CategoricalTerm, d::NamedTuple) = t.contrasts[d[t.sym], :]

test/terms.jl

@@ -22,6 +22,17 @@ mimestring(x) = mimestring(MIME"text/plain", x)
         @test t0.min == 1.0
         @test t0.max == 3.0
 
+        vals0m = [3, missing, 1]
+        t0m = concrete_term(t, vals0m)
+        @test string(t0m) == "aaa"
+        @test mimestring(t0m) == "aaa(continuous)"
+        # compute all these values to make sure the behavior of terms matches
+        # the behavior of other relevant packages
+        @test isequal(t0m.mean, mean(vals0m))
+        @test isequal(t0m.var, var(vals0m))
+        @test isequal(t0m.min, min(vals0m...))
+        @test isequal(t0m.max, max(vals0m...))
+
         t1 = concrete_term(t, [:a, :b, :c])
         @test t1.contrasts isa StatsModels.ContrastsMatrix{DummyCoding}
         @test string(t1) == "aaa"