Error while using predict function

1

Hi,
for a data when I try using a specific model to predict I get the following error. the predict function runs but gives and error when I am trying to convert it into a dataframe.

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image1611×769 177 KB

I checked the rows it had mentioned but was unable to find any missing values. Can anyone tell me what is going wrong.

2

Could you please let us know which version of Pumas that you are using and where you are running Pumas?

3

This should probably be 2.2.1 from Juliahub.com

4

Yes, I have been using this version from Juliahub

5

Sorry for this inconvenience, we have identified the issue. I have a solution for you below that you can use until the issue is resolved in a future version of Pumas.

using Pumas
sub1 = Subject(id = 1, events = DosageRegimen(100, rate = 100), time = 0.0:0.25:4)
sub2 = Subject(id = 2, time = 0.0:0.25:4)
mdlminimal = @model begin
@pre begin
  CL = 0.8
  Vc = 1.1
end
@dynamics Central1
@derived begin
  μ := @. Central / Vc
  y ~ @. Normal(μ, 0.1)
end
end
sim_sub = Subject.(simobs(mdlminimal, [sub1, sub2], (;)))

df_simobs = DataFrame(sim_sub)

df_pred = DataFrame(predict(mdlminimal, sim_sub, (;)))

This gives the same issue as you showed.

julia> df_pred = DataFrame(predict(mdlminimal, sim_sub, (;)))
ERROR: ArgumentError: column(s) amt, cmt, rate, duration, ss, ii, route and tad are missing from argument(s) 2
Stacktrace:
 [1] _vcat(dfs::Vector{AbstractDataFrame}; cols::Symbol)
   @ DataFrames ~/.julia/packages/DataFrames/JZ7x5/src/abstractdataframe/abstractdataframe.jl:1841
 [2] #reduce#130
   @ ~/.julia/packages/DataFrames/JZ7x5/src/abstractdataframe/abstractdataframe.jl:1761 [inlined]
 [3] reduce(::typeof(vcat), dfs::Vector{DataFrame})
   @ DataFrames ~/.julia/packages/DataFrames/JZ7x5/src/abstractdataframe/abstractdataframe.jl:1761
 [4] DataFrame(vpred::Vector{Pumas.SubjectPrediction{NamedTuple{(:y,), Tuple{Vector{Float64}}}, NamedTuple{(:y,), Tuple{Vector{Float64}}}, Nothing}}; include_covariates::Bool, include_observations::Bool, include_events::Bool)
   @ Pumas ~/.julia/dev/Pumas/src/estimation/diagnostics.jl:312
 [5] DataFrame(vpred::Vector{Pumas.SubjectPrediction{NamedTuple{(:y,), Tuple{Vector{Float64}}}, NamedTuple{(:y,), Tuple{Vector{Float64}}}, Nothing}})
   @ Pumas ~/.julia/dev/Pumas/src/estimation/diagnostics.jl:312
 [6] top-level scope
   @ REPL[26]:1

What I could do to work around this in my case is

mdl_pred = predict(mdlminimal, sim_sub, (;))
df_pred = reduce(vcat, DataFrame.(mdl_pred); cols=:union)

I am using DataFrame on the individual predictions and vertical concatenation (vcat) with the cols=:union keyword. You should similarly be able to write

df_pred = reduce(vcat, DataFrame.(Kartaza_pred); cols=:union)

Hope this helps.

1 Like
6

It works!! Thank you