Population Two Compartment Model with AOB transformation and Inter-subject Variance

[Generated automatically as a Fitting summary]

Model Description

Name:: iv_two_cmp_ab_isv
Title:: Population Two Compartment Model with AOB transformation and Inter-subject Variance
Author:: PoPy for PK/PD
Abstract:

Population One Compartment Model and Inter-subject Variance

Keywords:: two compartment model; iv_two_cmp_ab; additive noise; AOB transform
Input Script:: iv_two_cmp_ab_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[AOB]	2.6180	3.2136	0.5956	0.2275
f[ALPHA]	1.3090	0.2443	1.0647	0.8133
f[BETA]	0.1910	0.0267	0.1643	0.8604

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE_STD]	100.0000	4.9974	95.0026	0.9500

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[AOB_isv]	2.6180	0.9699	1.6481	0.6295
f[AOB_isv;ALPHA_isv]	0.0000	0.0000	0.0000	INF
f[AOB_isv;BETA_isv]	0.0000	0.0000	0.0000	INF
f[ALPHA_isv;AOB_isv]	0.0000	0.0000	0.0000	INF
f[ALPHA_isv]	0.2618	1.2487	0.9869	3.7696
f[ALPHA_isv;BETA_isv]	0.0000	0.0000	0.0000	INF
f[BETA_isv;AOB_isv]	0.0000	0.0000	0.0000	INF
f[BETA_isv;ALPHA_isv]	0.0000	0.0000	0.0000	INF
f[BETA_isv]	0.0382	0.1810	0.1428	3.7389

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

1848.3458

which required 1.29 iterations and took 100.89 seconds

Fitted f[X] values (after fitting)

f[AOB] = 3.2136
f[ALPHA] = 0.2443
f[BETA] = 0.0267
f[AOB_isv,ALPHA_isv,BETA_isv] = [
    [ 0.9699, 0.0000, 0.0000 ],
    [ 0.0000, 1.2487, 0.0000 ],
    [ 0.0000, 0.0000, 0.1810 ],
]
f[ANOISE_STD] = 4.9974

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[AOB] = 2.6180
f[ALPHA] = 1.3090
f[BETA] = 0.1910
f[AOB_isv,ALPHA_isv,BETA_isv] = [
    [ 2.6180, 0.0000, 0.0000 ],
    [ 0.0000, 0.2618, 0.0000 ],
    [ 0.0000, 0.0000, 0.0382 ],
]
f[ANOISE_STD] = 100.0000