Julia 1.0. GLM 1.0.0.
My Basic Example
julia> using GLM, StatsBase
julia> y=[1:10;] ; X= hcat( fill(1,10), y.^2, y.^3 ); lm1= GLM.lm(X, y);
Basic Questions
-
is the sigma (i.e.,
sqrt(deviance(lm)/dof_residual(lm)) already programmed into julia? -
is the f-statistic of the model (i.e., whether the model beats a constant) already available somewhere ?
My motivation is to try to write a function that prints regression output in a different format that I prefer. At first, I thought I would write a show function, but then I figured that the spirit of julia is for the user not to replace the basics, but to write different functionsâŚor maybe add a particular kind of keyword that dispatches to my function?
Less Important Question on ftest
- how does ftest work? The example from the docs does not work:
julia> using DataFrames, GLM, StatsBase
julia> dat = DataFrame(Treatment=[1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2.],
Result=[1.1, 1.2, 1, 2.2, 1.9, 2, .9, 1, 1, 2.2, 2, 2],
Other=[1, 1, 2, 1, 2, 1, 3, 1, 1, 2, 2, 1]);
julia> using StatsModels
julia> mod = lm(@formula(Result ~ 1 + Treatment), dat);
ERROR: cannot assign variable Base.mod from module Main
Stacktrace:
[1] top-level scope at none:0
julia> mod = lm(@formula(Result ~ 1 + Treatment), dat);
ERROR: cannot assign variable Base.mod from module Main
Stacktrace:
[1] top-level scope at none:0
so, I tried to make up my own simpler example and without DataFrame:
julia> using GLM, StatsBase
julia> y=[1:10;] ; X= hcat( fill(1,10), y.^2, y.^3 );
julia> y=[1:10;] ; XX= hcat( fill(1,10), y.^2, y.^3, y.^4 );
julia> lm1= GLM.lm(X, y); lm2= GLM.lm(XX, y);
julia> ftest(lm2, lm1)
Error showing value of type GLM.FTestResult{2}:
ERROR: MethodError: no method matching Array{String,2}(::Int64, ::Int64)
Closest candidates are:
Array{String,2}(::UndefInitializer, ::Int64, ::Int64) where T at boot.jl:396
Array{String,2}(::UndefInitializer, ::Int64...) where {T, N} at boot.jl:400
Array{String,2}(::UndefInitializer, ::Integer, ::Integer) where T at sysimg.jl:143
...
Stacktrace:
[1] show(::IOContext{REPL.Terminals.TTYTerminal}, ::GLM.FTestResult{2}) at /Users/ivo/.julia/packages/GLM/8TwVM/src/ftest.jl:110
Prediction?
in R, one gets a predicted value like this. How do I do it in this simple example?
julia> using DataFrames, GLM
julia> y=[1:10;] ; X= hcat( fill(1,10), y.^2, y.^3 ); lm1= GLM.lm(X, y);
julia> predict( lm1, [ 1.0, 2.0, 3.0 ])
ERROR: MethodError: no method matching predict(::LinearModel{LmResp{Array{Float64,1}},DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}}, ::Array{Float64,1})
I believe the implementation of these âbasicâ level statistics (including GLMs fitting) will be necessary for Juliaâs adoption to really explode (there are fields with millions of researchers almost exclusively (but intensively) using LMs or ANOVAs).