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

[Generated automatically as a Fitting 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_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

### Final objective value

    -163.411429465

which required 15 iterations and took 82.37 seconds

### Final fitted fixed effects

    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

### 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)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 1 | 0.5 | 1 | 0.5
f[CL] | 2.14619 | 1 | 1.14619 | 1.14619
f[V] | 22.0861 | 15 | 0.472407 | 7.08611

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.463588 | 0.2 | 1.31794 | 0.263588
f[ANOISE_STD] | 0.0617322 | 0.2 | 0.691339 | 0.138268

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 4.29671 | 0.01 | 428.671 | 4.28671