Merge f1a0669 into 8621065

JuliaStats · Sep 11, 2018 · d467307 · d467307
2 parents 8621065 + f1a0669
commit d467307
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Showing 5 changed files with 20 additions and 13 deletions.
diff --git a/docs/src/index.md b/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

diff --git a/src/GLM.jl b/src/GLM.jl
@@ -99,5 +99,6 @@ module GLM
     include("glmfit.jl")
     include("ftest.jl")
     include("negbinfit.jl")
+    include("deprecated.jl")
 
 end # module
diff --git a/src/deprecated.jl b/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)
diff --git a/src/lm.jl b/src/lm.jl
@@ -186,19 +186,23 @@ 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::Symbol = :none, level::Real = 0.95)
 
-Specifying `interval_type` will return a 3-column matrix with the prediction and
+If `interval` differs from `:none`, 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::Symbol=:none, level::Real = 0.95)
     retmean = newx * coef(mm)
-    interval_type == :confint || error("only :confint is currently implemented") #:predint will be implemented
+    if interval === :none
+        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

diff --git a/test/runtests.jl b/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
 
@@ -451,7 +451,7 @@ end
 
     Ylm = X * [0.8, 1.6] + 0.8randn(10)
     mm = fit(LinearModel, X, Ylm)
-    pred = predict(mm, newX, :confint)
+    pred = predict(mm, newX, interval=:confint)
 
     @test isapprox(pred[1,2], 0.6122189104014528)
     @test isapprox(pred[2,2], -0.33530477814532056)