Population Two Compartment Model and Inter-subject Variance

[Generated automatically as a Fitting summary]

Model Description

Name:: iv_two_cmp_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_k; proportional noise; additive noise
Input Script:: iv_two_cmp_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[K12]	0.5000	0.1893	0.3107	0.6214
f[K21]	0.5000	0.1229	0.3771	0.7542
f[KE]	0.5000	0.1034	0.3966	0.7931

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE_STD]	100.0000	4.9307	95.0693	0.9507

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE_isv]	0.0100	0.2973	0.2873	28.7261
f[KE_isv;K12_isv]	0.0000	0.0000	0.0000	INF
f[KE_isv;K21_isv]	0.0000	0.0000	0.0000	INF
f[K12_isv;KE_isv]	0.0000	0.0000	0.0000	INF
f[K12_isv]	0.0100	0.1608	0.1508	15.0767
f[K12_isv;K21_isv]	0.0000	0.0000	0.0000	INF
f[K21_isv;KE_isv]	0.0000	0.0000	0.0000	INF
f[K21_isv;K12_isv]	0.0000	0.0000	0.0000	INF
f[K21_isv]	0.0100	0.1855	0.1755	17.5462

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

1795.0662

which required 1.26 iterations and took 152.40 seconds

Fitted f[X] values (after fitting)

f[K12] = 0.1893
f[K21] = 0.1229
f[KE] = 0.1034
f[KE_isv,K12_isv,K21_isv] = [
    [ 0.2973, 0.0000, 0.0000 ],
    [ 0.0000, 0.1608, 0.0000 ],
    [ 0.0000, 0.0000, 0.1855 ],
]
f[ANOISE_STD] = 4.9307

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[K12] = 0.5000
f[K21] = 0.5000
f[KE] = 0.5000
f[KE_isv,K12_isv,K21_isv] = [
    [ 0.0100, 0.0000, 0.0000 ],
    [ 0.0000, 0.0100, 0.0000 ],
    [ 0.0000, 0.0000, 0.0100 ],
]
f[ANOISE_STD] = 100.0000