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

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | d1cmp_cl_isv_naive
Title: | One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0
Author: | Wright Dose Ltd
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: |

Failed to create compartment diagram

### True f[X] values

    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

    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

## Outputs

### Generating and Fitting 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)

### Fitted f[X] values

    f[KA] = 0.2087
    f[CL] = 3.0963
    f[V] = 14.8138
    f[PNOISE_STD] = 0.3766
    f[ANOISE_STD] = 0.1619
    f[CL_isv] = 0.0000

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    787.9714

### Final fitted objective value

    -200.9398

### Compare Main f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[KA] | 0.5 | 0.209 | 0.3 | 30.44% | 9.13e-02
f[CL] | 1 | 3.1 | 3 | 3.21% | 9.63e-02
f[V] | 15 | 14.8 | 20 | 25.93% | 5.19e+00

### Compare Noise f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2 | 0.377 | 0.1 | 276.65% | 2.77e-01
f[ANOISE_STD] | 0.2 | 0.162 | 0.05 | 223.71% | 1.12e-01

### Compare Variance f[X]

No Variance f[X] values to compare.