# Bioavailability and Lag

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | biolag_abs_bio
Title: | Bioavailability and Lag
Author: | Wright Dose Ltd
Abstract: |

One compartment model absorption dosing with bioavailability and lag parameters.

Keywords: | identifiability; bioavailability; lag
Input Script: | `biolag_abs_bio_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[ANOISE_STD] = 5.0000
    f[BIO] = 0.8000
    f[LAG] = 1.0000

## Outputs

### Final objective value

    61.1707

which required N. iterations and took 0.72 seconds

### Final fitted fixed effects

    f[KA] = 1.0000
    f[CL] = 7.9815
    f[V] = 88.3378
    f[ANOISE_STD] = 1.1181
    f[BIO] = 0.0000
    f[LAG] = 556991931180618173920440774875204562471103574122168320.0000

### 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] | 7.9815 | 1.0000 | 6.9815 | 6.9815
f[V] | 88.3378 | 15.0000 | 4.8892 | 73.3378
f[BIO] | 0.0000 | 0.8000 | 1.0000 | 0.8000
f[LAG] | 556991931180999970736126067831210468780642018055421952.0000 | 1.0000 | 556991931180999970736126067831210468780642018055421952.0000 | 556991931180999970736126067831210468780642018055421952.0000

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 1.1181 | 5.0000 | 0.7764 | 3.8819

### Compare Variance f[X]