Here is my code:
using Pumas using PumasUtilities using PharmaDatasets using DataFramesMeta # Read in Data pkfile = dataset("iv_sd_3") # Convert DataFrame into Collection of Subjects (Population) population = read_pumas(pkfile) # Model definition model = @model begin @param begin # here we define the parameters of the model tvcl ∈ RealDomain(; lower = 0.001) # typical clearance tvvc ∈ RealDomain(; lower = 0.001) # typical central volume of distribution Ω ∈ PDiagDomain(2) # between-subject variability σ ∈ RealDomain(; lower = 0.001) # residual error end @random begin # here we define random effects η ~ MvNormal(Ω) # multi-variate Normal with mean 0 and covariance matrix Ω end @pre begin # pre computations and other statistical transformations CL = tvcl * exp(η) Vc = tvvc * exp(η) end # here we define compartments and dynamics @dynamics Central1 # same as Central' = -(CL/Vc)*Central (see Pumas documentation) @derived begin # here is where we calculate concentration and add residual variability # tilde (~) means "distributed as" cp = @. 1000 * Central / Vc # ipred = A1/V dv ~ @. Normal(cp, σ) # dv ~ @. Normal(cp, sqrt(cp^2 * σ_prop^2 + σ_add^2)) end end # Parameter values params = (tvcl = 1.0, tvvc = 10.0, Ω = Diagonal([0.09, 0.09]), σ = 3.16) # Fit a base model fit_results = fit(model, population, params, Pumas.FOCE()) ins_results = DataFrame(inspect(fit_results)) all_names =names(ins_results) all_sel = @select(ins_results, :id, :time, :dv, :cp_pred, :dv_pred,:cp_ipred, :dv_ipred)
dv is the the observed concentration, but what is the different between cp_pred and dv_pred?
They show the exact same values. Why would it predict two of the same value?