Make CoefTable implement the Tables.jl interface (#629)

This allows retrieving the contents of a `CoefTable` object in a convenient form,
notably a `DataFrame`. To avoid introducing a dependency on Tables.jl, `CoefTable`
has to iterate `NamedTuple`s, so that it implements the row-table interface
implicitly. This is inefficient since `CoefTable` uses a column-based storage,
but given the typical size of such tables it should not matter.

Author: nalimilan

src/statmodels.jl

@@ -19,8 +19,12 @@ coefnames(model::StatisticalModel) = error("coefnames is not defined for $(typeo
 """
     coeftable(model::StatisticalModel; level::Real=0.95)
 
-Return a table of class `CoefTable` with coefficients and related statistics.
+Return a table with coefficients and related statistics of the model.
 `level` determines the level for confidence intervals (by default, 95%).
+
+The returned `CoefTable` object implements the
+[Tables.jl](https://github.com/JuliaData/Tables.jl/) interface, and can be
+converted e.g. to a `DataFrame` via `using DataFrames; DataFrame(coeftable(model))`.
 """
 coeftable(model::StatisticalModel) = error("coeftable is not defined for $(typeof(model)).")
 
@@ -420,6 +424,29 @@ mutable struct CoefTable
     end
 end
 
+Base.length(ct::CoefTable) = length(ct.cols[1])
+function Base.eltype(ct::CoefTable)
+    names = isempty(ct.rownms) ?
+        tuple(Symbol.(ct.colnms)...) :
+        tuple(Symbol("Name"), Symbol.(ct.colnms)...)
+    types = isempty(ct.rownms) ?
+        Tuple{eltype.(ct.cols)...} :
+        Tuple{eltype(ct.rownms), eltype.(ct.cols)...}
+    NamedTuple{names, types}
+end
+
+function Base.iterate(ct::CoefTable, i::Integer=1)
+    if i in 1:length(ct)
+        cols = getindex.(ct.cols, Ref(i))
+        nt = isempty(ct.rownms) ?
+            eltype(ct)(tuple(cols...)) :
+            eltype(ct)(tuple(ct.rownms[i], cols...))
+        (nt, i+1)
+    else
+        nothing
+    end
+end
+
 """
 Show a p-value using 6 characters, either using the standard 0.XXXX
 representation or as <Xe-YY.

test/statmodels.jl

@@ -7,27 +7,50 @@ v2 = ["Good", "Great", "Bad"]
 v3 = [1, 56, 2]
 v4 = [-12.56, 0.1326, 2.68e-16]
 v5 = [0.12, 0.3467, 1.345e-16]
-@test sprint(show, CoefTable(Any[v1, v2, v3, v4, v5],
-    ["Estimate", "Comments", "df", "t", "p"],
-    ["x1", "x2", "x3"], 5, 4)) == """
+ct = CoefTable(Any[v1, v2, v3, v4, v5],
+               ["Estimate", "Comments", "df", "t", "p"],
+               ["x1", "x2", "x3"], 5, 4)
+@test sprint(show, ct) == """
 ───────────────────────────────────────────────
          Estimate  Comments  df       t       p
 ───────────────────────────────────────────────
 x1    1.45666         Good    1  -12.56  0.1200
 x2  -23.14            Great  56    0.13  0.3467
 x3    1.56734e-13     Bad     2    0.00  <1e-15
 ───────────────────────────────────────────────"""
+@test length(ct) === 3
+@test eltype(ct) ==
+    NamedTuple{(:Name, :Estimate, :Comments, :df, :t, :p),
+               Tuple{String,Float64,String,Int,Float64,Float64}}
+@test collect(ct) == [
+    (Name = "x1", Estimate = 1.45666, Comments = "Good", df = 1, t = -12.56, p = 0.12)
+    (Name = "x2", Estimate = -23.14, Comments = "Great", df = 56, t = 0.1326, p = 0.3467)
+    (Name = "x3", Estimate = 1.56734e-13, Comments = "Bad", df = 2, t = 2.68e-16, p = 1.345e-16)
+]
 
 Random.seed!(10)
 m = rand(3,4)
-@test sprint(show, CoefTable(m, ["Estimate", "Stderror", "df", "p"], [], 4)) == """
+ct = CoefTable(m, ["Estimate", "Stderror", "df", "p"], [], 4)
+@test sprint(show, ct) == """
 ──────────────────────────────────────────
      Estimate   Stderror        df       p
 ──────────────────────────────────────────
 [1]  0.112582  0.0566454  0.381813  0.8198
 [2]  0.368314  0.120781   0.815104  0.6699
 [3]  0.344454  0.179574   0.242208  0.4531
 ──────────────────────────────────────────"""
+@test length(ct) === 3
+@test eltype(ct) ==
+    NamedTuple{(:Estimate, :Stderror, :df, :p),
+               Tuple{Float64,Float64,Float64,Float64}}
+@test collect(ct) == [
+    (Estimate = 0.11258244478647295, Stderror = 0.05664544616214151,
+     df = 0.38181274408522614, p = 0.8197779704008801)
+    (Estimate = 0.36831406658084287, Stderror = 0.12078054506961555,
+     df = 0.8151038332483567, p = 0.6699313951612162)
+    (Estimate = 0.3444540231363058, Stderror = 0.17957407667101322,
+     df = 0.2422083248151139, p = 0.4530583319523316)
+]
 
 @test sprint(show, PValue(1.0)) == "1.0000"
 @test sprint(show, PValue(1e-1)) == "0.1000"