One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

[Generated automatically as a Fitting summary]

Model Description

Name:

d1cmp_cl_isv_naive

Title:

One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

Author:

PoPy for PK/PD

Abstract:

Population one Compartment Model with Absorption and Inter-subject Variance

Here f[CL_isv] is not estimated it is set to zero.

Keywords:

one compartment model; dep_one_cmp_cl

Input Script:

d1cmp_cl_isv_naive_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	0.1818	0.3182	0.6363
f[CL]	1.0000	2.5519	1.5519	1.5519
f[V]	15.0000	20.1441	5.1441	0.3429

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.2000	0.4965	0.2965	1.4826
f[ANOISE_STD]	0.2000	0.1279	0.0721	0.3605

Compare Variance f[X]

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-163.1359

which required 1.17 iterations and took 16.60 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1818
f[CL] = 2.5519
f[V] = 20.1441
f[PNOISE_STD] = 0.4965
f[ANOISE_STD] = 0.1279
f[CL_isv] = 0.0000

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] = 0.0000