How to fit ODE system only on one @variables

Hello, :grin:
I have a small system of 2 differential equations whose @variables are N1 and N2, with 3 @parameters a, b, c. I have evolution data (experimental measurements) for the variable N1 only, named (time, data).
I would like to use DiffEqParamEstim.jl to fit my model on my data and to estimate the optimal a,b,c parameters.
How can I change the following cost function (and in particular the L2Loss I think) to fit the data only on N1 and not on N2 ?

cost_function = build_loss_objective(prob, Tsit5(), L2Loss(time, data),

optprob = Optimization.OptimizationProblem(cost_function, [0.0005, 0.009, 0.09])
result_bfgs = solve(optprob, BFGS())

Tanks ! :grin:

For instance, the DiffEqParamEstim.jl documentation give example of fit with synthetic data generated from the ODE system, so there is data for all @variables.

sol = solve(prob,Tsit5())
t = collect(range(0,stop=10,length=200))
using RecursiveArrayTools # for VectorOfArray
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)

Let me up the discussion one last time. It’s probably not very complicated (maybe even trivial), but I really don’t see how to do it. I’d like to avoid having to do a loss function “by hand”, but maybe it’s necessary.

Replied on github Fit a model on incomplete data · Issue #219 · SciML/DiffEqParamEstim.jl · GitHub