# Not able to get VPC plot

**URL:** <https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718>\
**Category:** vpc\
**Created:** [July 4, 2022, 5:23am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718 "2022-07-04T05:23:40Z")\
**Posts on this page:** 6\
**Page:** 1

<div class="post-metadata">

**Author:** ![sai\_matcha](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/sai_matcha/32/297_2.png) [@sai\_matcha](https://discourse.pumas.ai/u/sai_matcha)\
**Post date:** [July 4, 2022, 5:23am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/1 "2022-07-04T05:23:40Z")

</div>

Hi I am trying fit three different variables , this is how my derived block look like

```auto
    @derived begin
        ubp = @. Central/V2
        DV ~ @. Normal(ubp, ubp*(σ1_p))

        bp = @. Peripheral/ V3*pub
        BDV ~ @. Normal(bp,bp *(σ2_p))

        tc = @. (ubp + bp)
        PHNY ~ @. Normal(tc,tc*σ3_p)

    end 

```

I have used this VPC code.

```auto
pk_vpc = vpc(res_inspect_fm , 200; observations = [:DV,:BDV,:PHNY],
            # stratify_by = [:Dose],
             ensemblealg=EnsembleThreads())

```

here is the error

```auto
ERROR: MethodError: no method matching vpc(::Pumas.FittedPumasModelInspection{Pumas.FittedPumasModel{PumasModel{(TVK23 = 1, TVCL = 1, TVV2 = 1, TVV3 = 1, TVQ = 1, Ω = 3, σ1_p = 1, σ2_p = 1, σ3_p = 1, TVPUB = 1, θ = 4), 3, ParamSet{NamedTuple{(:TVK23, :TVCL, :TVV2, :TVV3, :TVQ, :Ω, :σ1_p, :σ2_p, :σ3_p, :TVPUB, :θ), Tuple{RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, PDiagDomain{PDMats.PDiagMat{Float64, Vector{Float64}}}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, VectorDomain{Vector{TransformVariables.Infinity{false}}, Vector{TransformVariables.Infinity{true}}, Vector{Float64}}}}}, Serialization. __deserialized_types__.var"#805#837", Serialization. __deserialized_types__.var"#806#838", Serialization. __deserialized_types__.var"#808#840", Serialization. __deserialized_types__.var"#810#842", Pumas.LinearODE, Serialization. __deserialized_types__.var"#811#843", Serialization. __deserialized_types__.var"#824#856", ModelingToolkit.ODESystem}, Vector{Subject{NamedTuple{(:DV, :BDV, :PHNY), Tuple{Vector{Union{Missing, Float64}}, Vector{Union{Missing, Float64}}, Vector{Union{Missing, Float64}}}}, T2, Vector{Pumas.Event{Float64, Float64, Float64, Float64, Float64, Float64, Int64}}, Vector{Float64}} where T2}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.BackTracking{Float64, Int64}, Nothing, Float64, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.BackTracking{Float64, Int64}, Nothing, Float64, Optim.Flat}}}, Bool, NamedTuple{(:f_limit_reached, :g_limit_reached, :h_limit_reached, :time_limit, :callback, :f_increased), NTuple{6, Bool}}}, Pumas.FOCE, Vector{Vector{Float64}}, NamedTuple{(:optimize_fn, :constantcoef, :omegas, :ensemblealg, :diffeq_options), Tuple{Pumas.DefaultOptimizeFN{Nothing, NamedTuple{(:show_trace, :store_trace, :extended_trace, :g_tol, :allow_f_increases), Tuple{Bool, Bool, Bool, Float64, Bool}}}, NamedTuple{(:TVV3, :TVQ), Tuple{Float64, Int64}}, Tuple{}, EnsembleThreads, NamedTuple{(), Tuple{}}}}, ParamSet{NamedTuple{(:TVK23, :TVCL, :TVV2, :TVV3, :TVQ, :Ω, :σ1_p, :σ2_p, :σ3_p, :TVPUB, :θ), Tuple{RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, Pumas.ConstDomain{Float64}, Pumas.ConstDomain{Int64}, PDiagDomain{PDMats.PDiagMat{Float64, Vector{Float64}}}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, RealDomain{Int64, TransformVariables.Infinity{true}, Int64}, VectorDomain{Vector{TransformVariables.Infinity{false}}, Vector{TransformVariables.Infinity{true}}, Vector{Float64}}}}}}, Vector{Pumas.SubjectPrediction{NamedTuple{(:ubp, :DV, :bp, :BDV, :tc, :PHNY), NTuple{6, Vector{Float64}}}, NamedTuple{(:ubp, :DV, :bp, :BDV, :tc, :PHNY), NTuple{6, Vector{Float64}}}, Nothing}}, Vector{Pumas.SubjectResidual{NamedTuple{(:DV, :BDV, :PHNY), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}}}, NamedTuple{(:DV, :BDV, :PHNY), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}}}, T3, Pumas.FOCE} where T3}, NamedTuple{(:ebes,), Tuple{Vector{NamedTuple{(:η,), Tuple{Vector{Float64}}}}}}, Nothing, Vector{Pumas.ConstantInterpolationStructArray{Vector{Float64}, StructArrays.StructVector{NamedTuple{(:id, :albonpub, :alb_cl, :scr_cl, :wt_pub, :pub, :k23, :CL, :V2, :V3, :Q, Symbol("##A##"), Symbol("##b##")), Tuple{String, Float64, Float64, Float64, Float64, Float64, Float64, Float64, Float64, Float64, Float64, StaticArrays.SMatrix{3, 3, Float64, 9}, StaticArrays.SVector{3, Int64}}}, NamedTuple{(:id, :albonpub, :alb_cl, :scr_cl, :wt_pub, :pub, :k23, :CL, :V2, :V3, :Q, Symbol("##A##"), Symbol("##b##")), Tuple{Vector{String}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{StaticArrays.SMatrix{3, 3, Float64, 9}}, Vector{StaticArrays.SVector{3, Int64}}}}, Int64}, Symbol}}, Vector{Pumas.ConstantInterpolationStructArray{Vector{Float64}, StructArrays.StructVector{NamedTuple{(:id,), Tuple{String}}, NamedTuple{(:id,), Tuple{Vector{String}}}, Int64}, Symbol}}}, ::Int64; observations=[:DV, :BDV, :PHNY], ensemblealg=EnsembleThreads())
Closest candidates are:
  vpc(::Pumas.AbstractFittedPumasModel, ::Integer; samples, kwargs...) at /builds/PumasAI/PumasSystemImages-jl/.julia/packages/Pumas/Ozwn0/src/estimation/vpc.jl:820
Stacktrace:
 [1] top-level scope
   @ ~/data/code/june_30_phenytoin/hennig.jl:3138

```

can some one help me to solve this error.

Thank you.

---

<div class="post-metadata">

**Author:** ![vijay](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/vijay/32/96_2.png) [@vijay](https://discourse.pumas.ai/u/vijay)\
**Post date:** [July 4, 2022, 10:06am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/2 "2022-07-04T10:06:37Z")

</div>

hi @sai_matcha - as the error states, it is a method error, which means that the function received an input that does not match the expected signature. `vpc` currently takes two kinds of inputs,

1. `vpc(fit)`
2. `vpc(model, data, param)`

You provided an `inspect` object to the `vpc` and hence it errored.

Second - the `observations` argument of `vpc` is there to specify which observation do you want to do a `vpc` for. You cannot run a `vpc` on all of them at the same time. if you look at the documentation of `vpc` [here](https://docs.pumas.ai/stable/analysis/vpc/#Overview) you will notice the following statement -

> - `observations`: The name of the dependent variable to use for the VPCs, passed in as a `Symbol` wrapped in an array. The default is the first dependent variable in the population. Even though the argument takes an array note that only a single derived variable at a time is supported.

---

<div class="post-metadata">

**Author:** ![sai\_matcha](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/sai_matcha/32/297_2.png) [@sai\_matcha](https://discourse.pumas.ai/u/sai_matcha)\
**Post date:** [July 11, 2022, 7:31am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/4 "2022-07-11T07:31:32Z")

</div>

Is thre a way to get “time after recent dose vs observations” vpc ?

Thanks

---

<div class="post-metadata">

**Author:** ![nikksramwani1](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/nikksramwani1/32/254_2.png) [@nikksramwani1](https://discourse.pumas.ai/u/nikksramwani1)\
**Post date:** [July 12, 2022, 8:56am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/5 "2022-07-12T08:56:07Z")

</div>

Hi Sai,

If the time for the recent (last dose) is identical for all the subjects, you can set the time points using the obstimes argument for the VPC.

If the time of dosing for all the subjects is not identical, you can use time after last dose as time for the estimation and then run a VPC. It might help.

---

<div class="post-metadata">

**Author:** ![sai\_matcha](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/sai_matcha/32/297_2.png) [@sai\_matcha](https://discourse.pumas.ai/u/sai_matcha)\
**Post date:** [July 12, 2022, 9:32am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/6 "2022-07-12T09:32:10Z")

</div>

Hi @nikksramwani1, Thanks for your response. I have mutliple doses for each subjects. If i set tad as time , then each subjects will have many dosing events at time 0. How to handle that ?

---

<div class="post-metadata">

**Author:** ![nikksramwani1](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pumas.ai/nikksramwani1/32/254_2.png) [@nikksramwani1](https://discourse.pumas.ai/u/nikksramwani1)\
**Post date:** [July 12, 2022, 11:15am UTC](https://discourse.pumas.ai/t/not-able-to-get-vpc-plot/718/7 "2022-07-12T11:15:38Z")

</div>

Hi @sai_matcha ,

You can filter the dataset for the recent (last dose), as you mentioned you want a VPC only for the last dose. Thank you.