Julia JuMP.jl中的Cox比例风险

我第一次尝试Julia中的JuMP.jl，但似乎无法避免错误。这是我的设置

使用数据帧、数据帧数据、跳转、Ipopt

#time to event
times = [143,164,188,189,190,192,206,209,213,216,220,227,230,234,246,265,304,216,244,
142,156,163,198,205,232,232,233,233,233,233,239,240,261,280,280,296,296,232,204,344];

#make censored data
is_censored = zeros(Int32, 40);
is_censored[18]=1
is_censored[19]=1
is_censored[39]=1
is_censored[40]=1

#treatment vs control
x1=ones(Int32,19)
x2=zeros(Int32,21)
x=append!(x1,x2)

#build DataFrame

using DataFrames

df = DataFrame();
df[:times]=times;
df[:is_censored]= is_censored;
df[:x]=x;
df

#sort df
df_sorted = sort!(df, cols = [order(:times)])

#make df_risk and df_uncensored
df_uncensored = @where(df_sorted, :is_censored .== 0)
df_risk = df_sorted

#cox partial likelihood



#use JuMP

##convert df to array

uncensored = convert(Array,df_uncensored[:x])
risk_set = convert(Array,df_risk[:x])

m = Model(solver=IpoptSolver(print_level=0))

@variable(m, β, start = 0.0)

@NLobjective(m, Max, sum(uncensored[i]*β-log*sum(exp(risk_set[j]*β) for j=1:length(risk_set)) for i=1:length(uncensored)))

最后一行是我所有的问题所在

@NLobjective(m, Max, sum(uncensored[i]*β-log*sum(exp(risk_set[j]*β) for j=1:length(risk_set)) for i=1:length(uncensored)))

我得到了错误

ERROR: MethodError: no method matching parseNLExpr_runtime(::JuMP.Model, ::Base.#log, ::Array{ReverseDiffSparse.NodeData,1}, ::Int64, ::Array{Float64,1})
Closest candidates are:
  parseNLExpr_runtime(::JuMP.Model, ::Number, ::Any, ::Any, ::Any) at /home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:196
  parseNLExpr_runtime(::JuMP.Model, ::JuMP.Variable, ::Any, ::Any, ::Any) at /home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:202
  parseNLExpr_runtime(::JuMP.Model, ::JuMP.NonlinearExpression, ::Any, ::Any, ::Any) at /home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:208
  ...
Stacktrace:
 [1] macro expansion at /home/icarus/.julia/v0.6/JuMP/src/parseExpr_staged.jl:489 [inlined]
 [2] macro expansion at /home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:226 [inlined]
 [3] macro expansion at /home/icarus/.julia/v0.6/JuMP/src/macros.jl:1086 [inlined]
 [4] anonymous at ./<missing>:?

错误：MethodError:没有与ParsenExpr_运行时匹配的方法（：：JuMP.Model，：：Base.#log，：：数组{ReverseDiffSparse.NodeData，1}，：：Int64，：：数组{Float64,1}）
最接近的候选人是：
parseNLExpr_运行时（：：JuMP.Model，：：Number，：：Any，：：Any，：：Any）位于/home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:196
parseNLExpr_运行时（：：JuMP.Model，：：JuMP.Variable，：：Any，：：Any，：：Any）位于/home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:202
parseNLExpr_运行时（：：JuMP.Model，：：JuMP.NonlinearExpression，：：Any，：：Any，：：Any）位于/home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:208
...
堆栈跟踪：
[1] 宏扩展位于/home/icarus/.julia/v0.6/JuMP/src/parsexpr_staged.jl:489[内联]
[2] 宏扩展位于/home/icarus/.julia/v0.6/JuMP/src/parsenlp.jl:226[内联]
[3] 宏扩展位于/home/icarus/.julia/v0.6/JuMP/src/macros.jl:1086[内联]
[4] 匿名地址：/？

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我一直在尝试使用的示例包括和您可能打算编写的

log(sum(exp(risk_set[j]*β) for j=1:length(risk_set)))

而不是

log*sum(exp(risk_set[j]*β) for j=1:length(risk_set))

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顺便说一句，很棒的包裹！跳跃非常有力！我打算继续使用它。