Flip Flop tutorial with inaccurate starting values

[Generated automatically as a Fitting summary]

Model Description

Name:: flip_flop_bad_pop
Title:: Flip Flop tutorial with inaccurate starting values
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 bad fit in the false minima.

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

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE]	0.4000	0.7720	0.3720	0.9300
f[V]	5.0000	1.3534	3.6466	0.7293
f[KA]	0.2000	0.0210	0.1790	0.8949

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0083	0.0417	0.8348
f[ANOISE]	0.1000	0.0543	0.0457	0.4571

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE_isv]	0.0200	0.1251	0.1051	5.2572
f[KE_isv;V_isv]	0.0000	-0.3020	0.3020	INF
f[KE_isv;KA_isv]	0.0000	-0.2594	0.2594	INF
f[V_isv;KE_isv]	0.0000	-0.3020	0.3020	INF
f[V_isv]	0.0200	0.9707	0.9507	47.5341
f[V_isv;KA_isv]	0.0000	0.8752	0.8752	INF
f[KA_isv;KE_isv]	0.0000	-0.2594	0.2594	INF
f[KA_isv;V_isv]	0.0000	0.8752	0.8752	INF
f[KA_isv]	0.0200	0.8327	0.8127	40.6361

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-194.0802

which required 1.30 iterations and took 90.87 seconds

Fitted f[X] values (after fitting)

f[KE] = 0.7720
f[V] = 1.3534
f[KA] = 0.0210
f[PNOISE] = 0.0083
f[ANOISE] = 0.0543
f[KE_isv,V_isv,KA_isv] = [
    [ 0.1251, -0.3020, -0.2594 ],
    [ -0.3020, 0.9707, 0.8752 ],
    [ -0.2594, 0.8752, 0.8327 ],
]

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] = 0.4000
f[V] = 5.0000
f[KA] = 0.2000
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 ],
]