Infer() error after fitting data

Estimation
1

i have updated the Pumas to latest version.
still i got an error while running code infer()

md = CSV.read("D:\\NONMEMnights\\mould\\julia\\12.3.2\\2comp_iv_data.csv", missingstring=".")

trans = read_pumas(md, id=:id, time=:TIME, dvs=[:dv],)

dian = @model begin
  @param   begin
    tvcl ∈ RealDomain(lower=0, init = 1.0)
    tvv ∈ RealDomain(lower=0, init = 20)
    tvQ ∈ RealDomain(lower = 0, init= 1)
    tvv2 ∈ RealDomain(lower = 0, init = 100)
    #Ω ∈ PDiagDomain(init=[0.09])
    σ_prop ∈ RealDomain(lower=0,init=0.04)
  end
#end
  @random begin
   η ~ MvNormal(Matrix{Float64}(0.01I, 1, 1))
#   η ~ MvNormal(Ω)
    #η ~ MvNormal(Ω)
  end
#end
  @pre begin
    CL = tvcl * exp(η[1])
    V  = tvv
    V2 = tvv2
    Q = tvQ
  end
#end
 #@covariates WT
#end
  #@dynamics ImmediateAbsorptionModel
    @dynamics begin
        #    Depot' =  -Ka*Depot
            Central' =  Q*(Peripheral/V2) - (CL/V)*Central - Q*(Central/V)

         Peripheral' =   Q*((Central/V)-(Peripheral/V2))
    end
#end
@vars begin
       Cent   :=  Central/V
       Peri  :=  Peripheral/V2
   end
  @derived begin
      cp = Cent
     # dv ~ @. Normal(cp,sqrt(cp^2*σ_prop))
      dv ~ @. Normal(cp, sqrt(cp^2*σ_prop)+eps())
    end
end

param =init_param(dian)

sims = simobs(dian,trans,param)
plot(sims)

res1 = fit(dian,trans,param,Pumas.FOCEI())


infer(res1)

and the error is

infer(res1)
Calculating: variance-covariance matrixERROR: PosDefException: matrix is not positive definite; Cholesky factorization failed.
Stacktrace:
 [1] chkposdef at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\lapack.jl:50 [inlined]
 [2] sygvd!(::Int64, ::Char, ::Char, ::Array{Float64,2}, ::Array{Float64,2}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\lapack.jl:5075
 [3] #eigen!#85 at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\symmetric.jl:677 [inlined]
 [4] eigen! at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\symmetric.jl:677 [inlined]
 [5] #eigen#68(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(eigen), ::Symmetric{Float64,Array{Float64,2}}, ::Symmetric{Float64,Array{Float64,2}}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:403
 [6] eigen(::Symmetric{Float64,Array{Float64,2}}, ::Symmetric{Float64,Array{Float64,2}}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:402
 [7] #vcov#234(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(vcov), ::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2,
:σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##9#16")),getfield(Main, Symbol("##10#17")),getfield(Main, Symbol("##11#18")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##12#19")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##14#21")),getfield(Main, Symbol("##15#22"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :σ_prop),NTuple{5,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1321
 [8] vcov at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1318 [inlined]
 [9] #infer#249(::Float64, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(infer), ::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv,
:tvQ, :tvv2, :σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##9#16")),getfield(Main, Symbol("##10#17")),getfield(Main, Symbol("##11#18")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##12#19")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##14#21")),getfield(Main, Symbol("##15#22"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :σ_prop),NTuple{5,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1422
 [10] infer(::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##9#16")),getfield(Main, Symbol("##10#17")),getfield(Main, Symbol("##11#18")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##12#19")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##14#21")),getfield(Main, Symbol("##15#22"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :σ_prop),NTuple{5,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1421
 [11] top-level scope at none:0
2

Most likely this happens because the sum of the outer product of the score contributions is almost singular. Could you please show us the output of res1 if you type it in the prompt window? Are you able to share the dataset?

3 
julia> res1
FittedPumasModel

Successful minimization:                true

Likelihood approximation:        Pumas.FOCEI
Objective function value:         -20.121217
Total number of observation records:      42
Number of active observation records:     42
Number of subjects:                        3

--------------------
           Estimate
--------------------
tvcl        0.56123
tvv        34.643
tvQ         2.802
tvv2      199.59
σ_prop      0.15429
--------------------

id TIME amt  	dv	mdv	wt	evid
1	0   50	.	1	70	1
1	2.5	.	0.26334	0	70	0
1	5	.	0.33196	0	70	0
1	10	.	0.28416	0	70	0
1	15	.	0.21946	0	70	0
1	30	.	0.116114	0	70	0
1	60	.	0.076738	0	70	0
1	90	.	0.061304	0	70	0
1	120	.	0.058616	0	70	0
1	180	.	0.053336	0	70	0
1	240	.	0.05046	0	70	0
1	300	.	0.034764	0	70	0
1	360	.	0.03508	0	70	0
1	480	.	0.038228	0	70	0
1	600	.	0.020326	0	70	0
2	0     100	   .           	1	60	1
2	2.5	.	2.1238	0	60	0
2	5	.	1.88695	0	60	0
2	10	.	1.12165	0	60	0
2	15	.	0.7418	0	60	0
2	30	.	0.56585	0	60	0
2	60	.	0.268845	0	60	0
2	90	.	0.418505	0	60	0
2	120	.	0.211785	0	60	0
2	180	.	0.238055	0	60	0
2	240	.	0.270265	0	60	0
2	300	.	0.112965	0	60	0
2	360	.	0.229935	0	60	0
2	480	.	0.17821	0	60	0
2	600	.	0.13506	0	60	0
3	0	200   	.	1	65	1
3	2.5	.	5.6644	0	65	0
3	5	.	6.1286	0	65	0
3	10	.	3.4058	0	65	0
3	15	.	2.1382	0	65	0
3	30	.	1.1687	0	65	0
3	60	.	0.75104	0	65	0
3	90	.	0.61568	0	65	0
3	120	.	0.66896	0	65	0
3	180	.	0.69688	0	65	0
3	240	.	0.61602	0	65	0
3	300	.	0.53642	0	65	0
3	360	.	0.46574	0	65	0
3	480	.	0.43812	0	65	0
3	600	.	0.27616	0	65	0
4

Is it because of the way ETA is specified with no variance term to estimate?

5

still the error persists after defining omega

md = CSV.read("D:\\NONMEMnights\\mould\\julia\\12.3.2\\2comp_iv_data.csv", missingstring=".")

trans = read_pumas(md, id=:id, time=:TIME, dvs=[:dv],)

dian = @model begin
  @param   begin
    tvcl ∈ RealDomain(lower=0, init = 1.0)
    tvv ∈ RealDomain(lower=0, init = 20)
    tvQ ∈ RealDomain(lower = 0, init= 1)
    tvv2 ∈ RealDomain(lower = 0, init = 100)
    Ω ∈ PDiagDomain(init=[0.09])
    σ_prop ∈ RealDomain(lower=0,init=0.04)
  end
#end
  @random begin
#   η ~ MvNormal(Matrix{Float64}(0.01I, 1, 1))
η ~ MvNormal(Ω)
    #η ~ MvNormal(Ω)
  end
#end
  @pre begin
    CL = tvcl * exp(η[1])
    V  = tvv
    V2 = tvv2
    Q = tvQ
  end
#end
 #@covariates WT
#end
  #@dynamics ImmediateAbsorptionModel
    @dynamics begin
        #    Depot' =  -Ka*Depot
            Central' =  Q*(Peripheral/V2) - (CL/V)*Central - Q*(Central/V)

         Peripheral' =   Q*(Central/V)-Q*(Peripheral/V2)
    end
#end
@vars begin
       Cent   :=  Central/V
       Peri  :=  Peripheral/V2
   end
  @derived begin
      cp = Cent
     # dv ~ @. Normal(cp,sqrt(cp^2*σ_prop))
      dv ~ @. Normal(cp, sqrt(cp^2*σ_prop)+eps())
    end
end

param =init_param(dian)

sims = simobs(dian,trans,param)
plot(sims)

res1 = fit(dian,trans,param,Pumas.FOCEI())


infer(res1)


julia> infer(res1)
Calculating: variance-covariance matrixERROR: PosDefException: matrix is not positive definite; Cholesky factorization failed.
Stacktrace:
 [1] chkposdef at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\lapack.jl:50 [inlined]
 [2] sygvd!(::Int64, ::Char, ::Char, ::Array{Float64,2}, ::Array{Float64,2}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\lapack.jl:5075
 [3] #eigen!#85 at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\symmetric.jl:677 [inlined]
 [4] eigen! at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\symmetric.jl:677 [inlined]
 [5] #eigen#68(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(eigen), ::Symmetric{Float64,Array{Float64,2}}, ::Symmetric{Float64,Array{Float64,2}}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:403
 [6] eigen(::Symmetric{Float64,Array{Float64,2}}, ::Symmetric{Float64,Array{Float64,2}}) at C:\Users\julia\AppData\Local\Julia-1.2.0\share\julia\stdlib\v1.2\LinearAlgebra\src\eigen.jl:402
 [7] #vcov#234(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(vcov), ::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2,
:Ω, :σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},PDiagDomain{PDMats.PDiagMat{Float64,Array{Float64,1}}},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##37#44")),getfield(Main, Symbol("##38#45")),getfield(Main, Symbol("##39#46")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##40#47")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##42#49")),getfield(Main, Symbol("##43#50"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :Ω, :σ_prop),Tuple{TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},Pumas.PDiagTransform,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1321
 [8] vcov at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1318 [inlined]
 [9] #infer#249(::Float64, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}, ::typeof(infer), ::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv,
:tvQ, :tvv2, :Ω, :σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},PDiagDomain{PDMats.PDiagMat{Float64,Array{Float64,1}}},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##37#44")),getfield(Main, Symbol("##38#45")),getfield(Main, Symbol("##39#46")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##40#47")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##42#49")),getfield(Main, Symbol("##43#50"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :Ω, :σ_prop),Tuple{TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},Pumas.PDiagTransform,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1422
 [10] infer(::Pumas.FittedPumasModel{PumasModel{ParamSet{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :Ω, :σ_prop),Tuple{RealDomain{Int64,TransformVariables.Infinity{true},Float64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},RealDomain{Int64,TransformVariables.Infinity{true},Int64},PDiagDomain{PDMats.PDiagMat{Float64,Array{Float64,1}}},RealDomain{Int64,TransformVariables.Infinity{true},Float64}}}},getfield(Main, Symbol("##37#44")),getfield(Main, Symbol("##38#45")),getfield(Main, Symbol("##39#46")),ODEProblem{Nothing,Tuple{Nothing,Nothing},false,Nothing,ODEFunction{false,getfield(Main, Symbol("##40#47")),UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},getfield(Main, Symbol("##42#49")),getfield(Main, Symbol("##43#50"))},Array{Subject{NamedTuple{(:dv,),Tuple{Array{Union{Missing, Float64},1}}},Nothing,Array{Pumas.Event{Float64,Float64,Float64,Float64,Float64,Float64,Int64},1},Array{Float64,1}},1},Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat},Float64,Array{Float64,1},Float64,Float64,Array{Optim.OptimizationState{Float64,Optim.BFGS{LineSearches.InitialStatic{Float64},LineSearches.BackTracking{Float64,Int64},getfield(Pumas, Symbol("##200#201")){NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}},Array{Float64,1}},Nothing,Optim.Flat}},1},Bool},Pumas.FOCEI,Array{Array{Float64,1},1},Tuple{},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},TransformVariables.TransformTuple{NamedTuple{(:tvcl, :tvv, :tvQ, :tvv2, :Ω, :σ_prop),Tuple{TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},TransformVariables.ShiftedExp{true,Int64},Pumas.PDiagTransform,TransformVariables.ShiftedExp{true,Int64}}}}}) at C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\packages\Pumas\6uorK\src\estimation\likelihoods.jl:1421
 [11] top-level scope at none:0
6

Fixing Ω as in the original version should be fine. I’m able to fit the model and compute the standard errors

julia> infer(res1)
Calculating: variance-covariance matrix. Done.
FittedPumasModelInference

Successful minimization:                true

Likelihood approximation:        Pumas.FOCEI
Objective function value:         -20.121217
Total number of observation records:      42
Number of active observation records:     42
Number of subjects:                        3

-------------------------------------------------------------
          Estimate        RSE                 95.0% C.I.
-------------------------------------------------------------
tvcl       0.56123     33.285        [  0.1951  ;   0.92737]
tvv       34.643       23.222        [ 18.875   ;  50.41   ]
tvQ        2.802       21.309        [  1.6317  ;   3.9723 ]
tvv2     199.59        13.236        [147.81    ; 251.37   ]
σ_prop     0.15429     55.535        [ -0.013649;   0.32223]
-------------------------------------------------------------

so which version of Pumas are you using? You can see that by typing ] status or Pkg.status() in at the prompt.

7

i have updated my Pumas before i run the codes.
these are my current version

(v1.2) pkg> status
    Status `C:\Users\Lenovo\.juliapro\JuliaPro_v1.2.0-1\environments\v1.2\Project.toml`
  [c52e3926] Atom v0.9.1
  [336ed68f] CSV v0.5.12
  [a93c6f00] DataFrames v0.19.4
  [7073ff75] IJulia v1.19.0
  [e5e0dc1b] Juno v0.7.1
  [91a5bcdd] Plots v0.26.3
  [4f2c3c20] Pumas v0.5.0
  [8bb1440f] DelimitedFiles
  [37e2e46d] LinearAlgebra
8

Hi Andreasnoack

what do mean by Fixing Ω as in the original version. Does it means any modifications to code. If possible can you please share the code if there are any modifications

9

In your original version you had

η ~ MvNormal(Matrix{Float64}(0.01I, 1, 1))

which means that you don’t estimate Ω, i.e. it’s fixed. I didn’t change anything in your original version.

10

Thank you
i have shared the package status in previous post, is that fine?

11

Yes Pumas 0.5 is the latest version so it isn’t clear why it’s failing. However, the information matrix of your problem is probably close to singular so small perturbations can make the Cholesky factorization either succeed or fail.

12

So we looked a bit at this, and it seems that what is happening is basically that indentifiability of tvv2 is not satisfied. This means that the calculation of a variance-covariance matrix is impossible (because the assumptions are not satisfied), and the result is that we throw a nonsense error (to most users anyway). We will try to make the error message more informative. Thanks for this example.

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