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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

0.2642

0.2358

0.4715

f[CL]

1.0000

2.7416

1.7416

1.7416

f[V]

15.0000

19.1975

4.1975

0.2798

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE_STD]

0.2000

0.1131

0.0869

0.4345

f[ANOISE_STD]

0.2000

0.0417

0.1583

0.7916

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[CL_isv]

0.0100

0.1647

0.1547

15.4697

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-371.3278

which required 1.20 iterations and took 413.60 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2642
f[CL] = 2.7416
f[V] = 19.1975
f[PNOISE_STD] = 0.1131
f[ANOISE_STD] = 0.0417
f[CL_isv] = 0.1647

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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