Flip Flop tutorial with low initial estimates of KA, V and CL

[Generated automatically as a Fitting summary]

Model Description

Name:: flip_flop_good_pop
Title:: Flip Flop tutorial with low initial estimates of KA, V and CL
Author:: PoPy for PK/PD
Abstract:

One compartment model with a depot leading into a central compartment.
Note another interpretation of this is that the model is just over parametrised. Because here
the KE and V parameters act independently unlike the CL/V and V parametrisation.
Here initial values of KE, KA, V are close to the flipped values, so we get a good fit near the true global minima.

Keywords:: one compartment model; flip flop; dep_one_cmp_k; good start values
Input Script:: flip_flop_good_pop_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE]	0.0200	0.0494	0.0294	1.4692
f[V]	30.0000	39.4926	9.4926	0.3164
f[KA]	0.3000	0.5716	0.2716	0.9055

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0069	0.0431	0.8614
f[ANOISE]	0.1000	0.0536	0.0464	0.4644

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE_isv]	0.0200	0.0439	0.0239	1.1967
f[KE_isv;V_isv]	0.0000	-0.0084	0.0084	INF
f[KE_isv;KA_isv]	0.0000	0.0091	0.0091	INF
f[V_isv;KE_isv]	0.0000	-0.0084	0.0084	INF
f[V_isv]	0.0200	0.0374	0.0174	0.8705
f[V_isv;KA_isv]	0.0000	0.0168	0.0168	INF
f[KA_isv;KE_isv]	0.0000	0.0091	0.0091	INF
f[KA_isv;V_isv]	0.0000	0.0168	0.0168	INF
f[KA_isv]	0.0200	0.0439	0.0239	1.1944

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-282.2522

which required 1.28 iterations and took 153.32 seconds

Fitted f[X] values (after fitting)

f[KE] = 0.0494
f[V] = 39.4926
f[KA] = 0.5716
f[PNOISE] = 0.0069
f[ANOISE] = 0.0536
f[KE_isv,V_isv,KA_isv] = [
    [ 0.0439, -0.0084, 0.0091 ],
    [ -0.0084, 0.0374, 0.0168 ],
    [ 0.0091, 0.0168, 0.0439 ],
]

Fitted parameter .csv files

Fixed Effects:: fx_params.csv (fit)
Random Effects:: rx_params.csv (fit)
Model params:: mx_params.csv (fit)
State values:: sx_params.csv (fit)
Predictions:: px_params.csv (fit)
Likelihoods:: lx_params.csv (fit)

Inputs

Input Data:: cx_obs_params.csv

Starting f[X] values (before fitting)

f[KE] = 0.0200
f[V] = 30.0000
f[KA] = 0.3000
f[PNOISE] = 0.0500
f[ANOISE] = 0.1000
f[KE_isv,V_isv,KA_isv] = [
    [ 0.0200, 0.0000, 0.0000 ],
    [ 0.0000, 0.0200, 0.0000 ],
    [ 0.0000, 0.0000, 0.0200 ],
]