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One Compartment Model with Absorption and Inter-subject Variance f[CL_isv]=0.2

[Generated automatically as a Fitting summary]

Model Description

Name:d1cmp_cl_isv
Title:One Compartment Model with Absorption and Inter-subject Variance f[CL_isv]=0.2
Author:PoPy for PK/PD
Abstract:
Population one Compartment Model with Absorption and Inter-subject Variance
Keywords:one compartment model; dep_one_cmp_cl
Input Script:d1cmp_cl_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 0.5000 0.2802 0.2198 0.4396
f[CL] 1.0000 3.2273 2.2273 2.2273
f[V] 15.0000 19.8391 4.8391 0.3226

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE_STD] 0.2000 0.0809 0.1191 0.5953
f[ANOISE_STD] 0.2000 0.0373 0.1627 0.8134

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[CL_isv] 0.0100 0.1766 0.1666 16.6568

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-430.9290

which required 1.21 iterations and took 452.55 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2802
f[CL] = 3.2273
f[V] = 19.8391
f[PNOISE_STD] = 0.0809
f[ANOISE_STD] = 0.0373
f[CL_isv] = 0.1766

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[KA] = 0.5000
f[CL] = 1.0000
f[V] = 15.0000
f[PNOISE_STD] = 0.2000
f[ANOISE_STD] = 0.2000
f[CL_isv] = 0.0100
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