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

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | d1cmp_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: | `d1cmp_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

    -200.9398

which required 1.21 iterations and took 176.17 seconds

### Final fitted fixed effects

    f[KA] = 0.2087
    f[CL] = 3.0963
    f[V] = 14.8138
    f[PNOISE_STD] = 0.3766
    f[ANOISE_STD] = 0.1619
    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 | 0.2087 | 0.5826 | 0.2913
f[CL] | 1.0000 | 3.0963 | 2.0963 | 2.0963
f[V] | 15.0000 | 14.8138 | 0.0124 | 0.1862

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2000 | 0.3766 | 0.8832 | 0.1766
f[ANOISE_STD] | 0.2000 | 0.1619 | 0.1907 | 0.0381

### Compare Variance f[X]