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

[Generated automatically as a Tutorial 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.pyml`

Diagram:

## Comparison

### True objective value

    2777.3504

### Final fitted objective value

    -163.1359

### Compare Main f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[KA] | 0.5 | 0.182 | 0.3 | 1.18e-01 | 39.38%
f[CL] | 1 | 2.55 | 3 | 4.48e-01 | 14.94%
f[V] | 15 | 20.1 | 20 | 1.44e-01 | 0.72%

### Compare Noise f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2 | 0.497 | 0.1 | 3.97e-01 | 396.53%
f[ANOISE_STD] | 0.2 | 0.128 | 0.05 | 7.79e-02 | 155.81%

### Compare Variance f[X]

No Variance f[X] values to compare.

## Outputs

### 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

### Generated data .csv file

Synthetic Data:

  `synthetic_data.csv`

### Gen and Fit Summaries

- Gen: One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0 (gen)

- Fit: One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0 (fit)

## Inputs

### True f[X] values (for simulation)

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv] = 0.2000

### 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