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

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | dep_one_cmp_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: | `dep_one_cmp_cl_isv_naive.pyml`
Diagram: |

Failed to create compartment diagram

### True f[X] values

    f[KA] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv] = 0.2

### Starting f[X] values

    f[KA] = 0.5
    f[CL] = 1
    f[V] = 15
    f[PNOISE_STD] = 0.2
    f[ANOISE_STD] = 0.2
    f[CL_isv] = 0

## 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] = 1
    f[CL] = 1
    f[V] = 7.3224
    f[PNOISE_STD] = 0.62876
    f[ANOISE_STD] = 0.12415
    f[CL_isv] = 0

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    2777.350573

### Final fitted objective value

    -60.460788808

### Compare Main f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[KA] | 0.5 | 1 | 0.3 | 233.33% | 7.00e-01
f[CL] | 1 | 1 | 3 | 66.67% | 2.00e+00
f[V] | 15 | 7.32 | 20 | 63.39% | 1.27e+01

### Compare Noise f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2 | 0.629 | 0.1 | 528.76% | 5.29e-01
f[ANOISE_STD] | 0.2 | 0.124 | 0.05 | 148.31% | 7.42e-02

### Compare Variance f[X]

No Variance f[X] values to compare.