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

[Generated automatically as a Fitting 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_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] = 0.0000

## Outputs

### Final objective value

    -163.1359

which required 1.17 iterations and took 77.15 seconds

### Final fitted fixed effects

    f[KA] = 0.1818
    f[CL] = 2.5518
    f[V] = 20.1377
    f[PNOISE_STD] = 0.4966
    f[ANOISE_STD] = 0.1279
    f[CL_isv] = 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 | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 0.5000 | 0.1818 | 0.6364 | 0.3182
f[CL] | 1.0000 | 2.5518 | 1.5518 | 1.5518
f[V] | 15.0000 | 20.1377 | 0.3425 | 5.1377

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2000 | 0.4966 | 1.4829 | 0.2966
f[ANOISE_STD] | 0.2000 | 0.1279 | 0.3605 | 0.0721

### Compare Variance f[X]