Population One Compartment Model and Inter-subject Variance

[Generated automatically as a Fitting summary]

Model Description

Name:: iv_one_cmp_isv
Title:: Population One Compartment Model and Inter-subject Variance
Author:: PoPy for PK/PD
Abstract:

Population One Compartment Model and Inter-subject Variance

Keywords:: one compartment model; iv_one_cmp_k; additive noise
Input Script:: iv_one_cmp_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE]	0.5000	0.0753	0.4247	0.8495

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE_STD]	100.0000	4.9669	95.0331	0.9503

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE_isv]	0.0100	0.2379	0.2279	22.7917

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

1780.5526

which required 1.16 iterations and took 47.84 seconds

Fitted f[X] values (after fitting)

f[KE] = 0.0753
f[KE_isv] = 0.2379
f[ANOISE_STD] = 4.9669

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] = 0.5000
f[KE_isv] = 0.0100
f[ANOISE_STD] = 100.0000