# 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] = 0.5000
    f[CL] = 1.0000
    f[V] = 15.0000
    f[PNOISE_STD] = 0.2000
    f[ANOISE_STD] = 0.2000
    f[CL_isv] = 0.0100
    f[CL_iov] = 0.0000

## Outputs

### Final objective value

    -190.0814

which required N. iterations and took 177.31 seconds

### Final fitted fixed effects

    f[KA] = 1.0000
    f[CL] = 2.2395
    f[V] = 24.3910
    f[PNOISE_STD] = 0.4529
    f[ANOISE_STD] = 0.0599
    f[CL_isv] = 0.1276
    f[CL_iov] = 0.0000

### 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] | 1.0000 | 0.5000 | 1.0000 | 0.5000
f[CL] | 2.2395 | 1.0000 | 1.2395 | 1.2395
f[V] | 24.3910 | 15.0000 | 0.6261 | 9.3910

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.4529 | 0.2000 | 1.2646 | 0.2529
f[ANOISE_STD] | 0.0599 | 0.2000 | 0.7004 | 0.1401

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.1276 | 0.0100 | 11.7552 | 0.1176