Error in predict() with just a constant term

[RobertGregg]

using GLM
using DataFrames

df1 = DataFrame(y = rand(10))
df2 = DataFrame(y = rand(10))

model = lm(@formula(y~1),df1)

y = predict(model,df2)

Gives the following error:
ERROR: ArgumentError: collection must be non-empty

I realize this is a weird edge case, but I think it should just return mean(df1.y) for each prediction.

[andreasnoack]

In addition, also the show method is broken in this case

julia> model
LinearModel

y ~ 1

Error showing value of type LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:
ERROR: MethodError: no method matching StatsBase.CoefTable(::Matrix{Float64}, ::Vector{String}, ::String, ::Int64, ::Int64)

Closest candidates are:
  StatsBase.CoefTable(::Matrix, ::Vector, ::Vector, ::Int64, ::Int64)
   @ StatsBase ~/.julia/packages/StatsBase/ebrT3/src/statmodels.jl:23
  StatsBase.CoefTable(::Vector, ::Vector, ::Vector, ::Int64, ::Int64)
   @ StatsBase ~/.julia/packages/StatsBase/ebrT3/src/statmodels.jl:10
  StatsBase.CoefTable(::Matrix, ::Vector, ::Vector, ::Int64)
   @ StatsBase ~/.julia/packages/StatsBase/ebrT3/src/statmodels.jl:23
  ...

Stacktrace:
  [1] coeftable(mm::LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}; level::Float64)
    @ GLM ~/.julia/dev/GLM/src/lm.jl:257
  [2] coeftable
    @ ~/.julia/dev/GLM/src/lm.jl:243 [inlined]
  [3] show(io::IOContext{Base.TTY}, obj::LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}})
    @ GLM ~/.julia/dev/GLM/src/linpred.jl:324
  [4] show(io::IOContext{Base.TTY}, ::MIME{Symbol("text/plain")}, x::LinearModel{GLM.LmResp{Vector{…}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{…}}})
    @ Base.Multimedia ./multimedia.jl:47