Julia:不能将 `convert` Array{Number,1} 类型的对象转换为 GLM.LmResp 类型的对象

Question

我正在逐行构建一个 DataFrame，然后对其进行回归。为简单起见，代码为:

using DataFrames
using GLM

df = DataFrame(response = Number[])
for i in 1:10
    df = vcat(df, DataFrame(response = rand()))
end

fit(LinearModel, @formula(response ~ 1), df)

我得到错误:

ERROR: LoadError: MethodError: Cannot `convert` an object of type Array{Number,1} to an object of type GLM.LmResp
This may have arisen from a call to the constructor GLM.LmResp(...),
since type constructors fall back to convert methods.
Stacktrace:
 [1] fit(::Type{GLM.LinearModel}, ::Array{Float64,2}, ::Array{Number,1}) at ~/.julia/v0.6/GLM/src/lm.jl:140
 [2] #fit#44(::Dict{Any,Any}, ::Array{Any,1}, ::Function, ::Type{GLM.LinearModel}, ::StatsModels.Formula, ::DataFrames.DataFrame) at ~/.julia/v0.6/StatsModels/src/statsmodel.jl:72
 [3] fit(::Type{GLM.LinearModel}, ::StatsModels.Formula, ::DataFrames.DataFrame) at ~/.julia/v0.6/StatsModels/src/statsmodel.jl:66
 [4] include_from_node1(::String) at ./loading.jl:576
 [5] include(::String) at ./sysimg.jl:14
while loading ~/test.jl, in expression starting on line 10

对线性回归的调用与 regression in "Introducing Julia" 非常相似:

linearmodel = fit(LinearModel, @formula(Y1 ~ X1), anscombe)

问题是什么？

Answer 1

几个小时后，我意识到 GLM 需要具体类型，而 Number 是一种抽象类型(尽管 documentation for GLM.LmResp 在撰写本文时对此只字未提，仅“封装了线性模型的响应”) .解决方案是将声明更改为具体类型，例如 Float64:

using DataFrames
using GLM

df = DataFrame(response = Float64[])
for i in 1:10
    df = vcat(df, DataFrame(response = rand()))
end

fit(LinearModel, @formula(response ~ 1), df)

输出:

StatsModels.DataFrameRegressionModel{GLM.LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,Base.LinAlg.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}

Formula: response ~ +1

Coefficients:
             Estimate Std.Error t value Pr(>|t|)
(Intercept)  0.408856 0.0969961 4.21518   0.0023

类型必须是具体的，例如带有 df = DataFrame(response = Real[]) 的抽象类型 Real 失败并显示更有用的错误消息:

ERROR: LoadError: `float` not defined on abstractly-typed arrays; please convert to a more specific type

或者，您可以在构建数据框后转换为 Real:

using DataFrames
using GLM

df = DataFrame(response = Number[])
for i in 1:10
    df = vcat(df, DataFrame(response = rand()))
end

df2 = DataFrame(response = map(Real, df[:response]))

fit(LinearModel, @formula(response ~ 1), df2)

之所以可行，是因为转换为 Real 实际上会转换为 Float64:

julia> typeof(df2[:response])
Array{Float64,1}

我提交了一份 issue with GLM改进错误消息。