Population Two Compartment Model and Inter-subject Variance

[Generated automatically as a Fitting summary]

Model Description

Name:: iv_two_cmp_cl_isv
Title:: Population Two Compartment Model 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_cl; proportional noise; additive noise
Input Script:: iv_two_cmp_cl_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL]	1.0000	2.3258	1.3258	1.3258
f[V1]	15.0000	7.3693	7.6307	0.5087
f[Q]	1.0000	1.0653	0.0653	0.0653
f[V2]	25.0000	10.1994	14.8006	0.5920

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0123	0.0377	0.7537
f[ANOISE]	0.0500	0.0093	0.0407	0.8149

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.2000	0.1914	0.0086	0.0430
f[CL_isv;V1_isv]	0.0000	0.2094	0.2094	INF
f[CL_isv;Q_isv]	0.0000	-0.0294	0.0294	INF
f[CL_isv;V2_isv]	0.0000	-0.0940	0.0940	INF
f[V1_isv;CL_isv]	0.0000	0.2094	0.2094	INF
f[V1_isv]	0.1000	0.6863	0.5863	5.8627
f[V1_isv;Q_isv]	0.0000	-0.0546	0.0546	INF
f[V1_isv;V2_isv]	0.0000	0.1520	0.1520	INF
f[Q_isv;CL_isv]	0.0000	-0.0294	0.0294	INF
f[Q_isv;V1_isv]	0.0000	-0.0546	0.0546	INF
f[Q_isv]	0.0500	0.1462	0.0962	1.9239
f[Q_isv;V2_isv]	0.0000	0.0294	0.0294	INF
f[V2_isv;CL_isv]	0.0000	-0.0940	0.0940	INF
f[V2_isv;V1_isv]	0.0000	0.1520	0.1520	INF
f[V2_isv;Q_isv]	0.0000	0.0294	0.0294	INF
f[V2_isv]	0.2000	0.6832	0.4832	2.4161

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-1654.2493

which required 1.30 iterations and took 153.96 seconds

Fitted f[X] values (after fitting)

f[CL] = 2.3258
f[V1] = 7.3693
f[Q] = 1.0653
f[V2] = 10.1994
f[PNOISE] = 0.0123
f[ANOISE] = 0.0093
f[CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1914, 0.2094, -0.0294, -0.0940 ],
    [ 0.2094, 0.6863, -0.0546, 0.1520 ],
    [ -0.0294, -0.0546, 0.1462, 0.0294 ],
    [ -0.0940, 0.1520, 0.0294, 0.6832 ],
]

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[CL] = 1.0000
f[V1] = 15.0000
f[Q] = 1.0000
f[V2] = 25.0000
f[PNOISE] = 0.0500
f[ANOISE] = 0.0500
f[CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.2000, 0.0000, 0.0000, 0.0000 ],
    [ 0.0000, 0.1000, 0.0000, 0.0000 ],
    [ 0.0000, 0.0000, 0.0500, 0.0000 ],
    [ 0.0000, 0.0000, 0.0000, 0.2000 ],
]