JuMP.jlのCox Proportional Hazard

Question

Juliaで初めてJuMP.jlを試していますが、エラーを回避できないようです。ここに私のセットアップです。データフレームを使用して

は、DataFramesMeta、ジャンプ、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))) 

は、最後の行は私の問題のすべてが

@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))) 

が、私はエラー私が持っている

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>:? 

例を取得しているところでありますのように使用しようとしていますmaximum likelihood exampleとoptimal control

Answer 1

あなたは、おそらく方法で代わりに

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