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  • Documentation version: 1.3.1

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

0.2087

0.2913

0.5827

f[CL]

1.0000

3.0963

2.0963

2.0963

f[V]

15.0000

14.8134

0.1866

0.0124

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE_STD]

0.2000

0.3766

0.1766

0.8830

f[ANOISE_STD]

0.2000

0.1619

0.0381

0.1907

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

-200.9398

which required 1.23 iterations and took 24.04 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2087
f[CL] = 3.0963
f[V] = 14.8134
f[PNOISE_STD] = 0.3766
f[ANOISE_STD] = 0.1619
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
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