JuMP.jl中的Cox比例危險

我正在第一次嘗試Julia中的JuMP.jl，似乎無法解決錯誤。這是我的設置。JuMP.jl中的Cox比例危險

使用DataFrames，DataFramesMeta，跳躍，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)))

最後一行就是我所有的問題都

@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)))

我得到的錯誤

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>:?

例子我有一直試圖使用maximum likelihood example和optimal control

來源

2017-05-26 Alex

你可能是指的方式來寫

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))

來源

2017-05-26 17:14:45 mlubin

大包！ JuMP非常強大！我打算繼續使用它。 – Alex

JuMP.jl中的Cox比例危險

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