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

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | dep_one_cmp_cl_iov_naive
Title: | One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0
Author: | Wright Dose Ltd
Abstract: |

Population one Compartment Model with Absorption and Inter-occasion Variance
Here f[CL_iov] is not estimated it is set to zero.

Keywords: | one compartment model; dep_one_cmp_cl; iov
Input Script: | `dep_one_cmp_cl_iov_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
    f[CL_iov] = 0.1

### 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] = 0.01
    f[CL_iov] = 0

## Outputs

### Generating and Fitting Summaries

- Gen: One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0 (gen)
- Fit: One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0 (fit)

### Fitted f[X] values

    f[KA] = 1
    f[CL] = 2.1462
    f[V] = 22.086
    f[PNOISE_STD] = 0.46359
    f[ANOISE_STD] = 0.061732
    f[CL_isv] = 4.2967
    f[CL_iov] = 0

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    242.737153435

### Final fitted objective value

    -163.412466343

### 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 | 2.15 | 3 | 28.46% | 8.54e-01
f[V] | 15 | 22.1 | 20 | 10.43% | 2.09e+00

### Compare Noise f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2 | 0.464 | 0.1 | 363.59% | 3.64e-01
f[ANOISE_STD] | 0.2 | 0.0617 | 0.05 | 23.46% | 1.17e-02

### Compare Variance f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[CL_isv] | 0.01 | 4.3 | 0.2 | 2048.35% | 4.10e+00