Merge d7cc9c2 into 8621065

docs/src/index.md

@@ -81,16 +81,17 @@ julia> predict(ols)
  4.33333
  6.83333
 
-```
-<!-- Andreas Noack: As of 9 May 2018 this example doesn't work so I've temporarily commented it out
 julia> newX = DataFrame(X=[2,3,4]);
 
-julia> predict(ols, newX, :confint)
+julia> predict(ols, newX, interval=:confint)
  3×3 Array{Float64,2}:
   4.33333  1.33845   7.32821
   6.83333  2.09801  11.5687
   9.33333  1.40962  17.257
-The columns of the matrix are prediction, 95% lower and upper confidence bounds -->
+```
+
+The columns of the matrix are prediction, 95% lower and upper confidence bounds
+.
 
 ### Probit Regression:
 ```jldoctest

src/GLM.jl

@@ -99,5 +99,6 @@ module GLM
     include("glmfit.jl")
     include("ftest.jl")
     include("negbinfit.jl")
+    include("deprecated.jl")
 
 end # module

src/deprecated.jl

@@ -0,0 +1 @@
+@Base.deprecate predict(mm::LinearModel, newx::AbstractMatrix, interval::Symbol, level::Real = 0.95) predict(mm, newx; interval=interval, level=level)

src/lm.jl

@@ -186,19 +186,25 @@ function coeftable(mm::LinearModel)
               ["x$i" for i = 1:size(mm.pp.X, 2)], 4)
 end
 
-predict(mm::LinearModel, newx::AbstractMatrix) = newx * coef(mm)
-
 """
-    predict(mm::LinearModel, newx::AbstractMatrix, interval_type::Symbol, level::Real = 0.95)
+    predict(mm::LinearModel, newx::AbstractMatrix;
+            interval::Union{Symbol,Nothing} = nothing, level::Real = 0.95)
 
-Specifying `interval_type` will return a 3-column matrix with the prediction and
+If `interval` is `nothing` (the default), return a vector with the predicted values
+for model `mm` and new data `newx`.
+Otherwise, return a 3-column matrix with the prediction and
 the lower and upper confidence bounds for a given `level` (0.95 equates alpha = 0.05).
-Valid values of `interval_type` are `:confint` delimiting the  uncertainty of the
+Valid values of `interval` are `:confint` delimiting the  uncertainty of the
 predicted relationship, and `:predint` delimiting estimated bounds for new data points.
 """
-function predict(mm::LinearModel, newx::AbstractMatrix, interval_type::Symbol, level::Real = 0.95)
+function predict(mm::LinearModel, newx::AbstractMatrix;
+                 interval::Union{Symbol,Nothing}=nothing, level::Real = 0.95)
     retmean = newx * coef(mm)
-    interval_type == :confint || error("only :confint is currently implemented") #:predint will be implemented
+    if interval === nothing
+        return retmean
+    elseif interval !== :confint
+        error("only :confint is currently implemented") #:predint will be implemented
+    end
     length(mm.rr.wts) == 0 || error("prediction with confidence intervals not yet implemented for weighted regression")
 
     R = cholesky!(mm.pp).U #get the R matrix from the QR factorization

test/runtests.jl

@@ -438,7 +438,7 @@ end
     @test isapprox(predict(gm12, newX, offset=newoff),
         logistic.(newX * coef(gm12) .+ newoff))
 
-        # Prediction from DataFrames
+    # Prediction from DataFrames
     d = convert(DataFrame, X)
     d[:y] = Y
 
@@ -451,18 +451,18 @@ end
 
     Ylm = X * [0.8, 1.6] + 0.8randn(10)
     mm = fit(LinearModel, X, Ylm)
-    pred = predict(mm, newX, :confint)
-
-    @test isapprox(pred[1,2], 0.6122189104014528)
-    @test isapprox(pred[2,2], -0.33530477814532056)
-    @test isapprox(pred[3,2], 1.340413688904295)
-    @test isapprox(pred[4,2], 0.02118806218116165)
-    @test isapprox(pred[5,2], 0.8543142404183606)
-    @test isapprox(pred[1,3], 2.6853964084909836)
-    @test isapprox(pred[2,3], 1.2766396685055916)
-    @test isapprox(pred[3,3], 3.6617479283005894)
-    @test isapprox(pred[4,3], 0.6477623101170038)
-    @test isapprox(pred[5,3], 2.564532433982956)
+    pred1 = predict(mm, newX)
+    pred2 = predict(mm, newX, interval=:confint)
+
+    @test pred1 == pred2[:, 1] ≈
+        [1.6488076594462182, 0.4706674451801356, 2.5010808086024423,
+         0.3344751861490827, 1.7094233372006582]
+    @test pred2[:, 2:3] ≈ [ 0.6122189104014528  2.6853964084909836
+                           -0.33530477814532056 1.2766396685055916
+                            1.340413688904295   3.6617479283005894
+                            0.02118806218116165 0.6477623101170038
+                            0.8543142404183606  2.564532433982956]
+
 end
 
 @testset "F test for model comparison" begin