# 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

    38.7412

which required 1.30 iterations and took 11.58 seconds

### Final fitted fixed effects

    f[KA] = 1.0000
    f[CL] = 2.1152
    f[V] = 8.3532
    f[ANOISE_STD] = 0.8936
    f[BIO] = 0.2407
    f[LAG] = 9.2028

### 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 | 1.0000 | 1.0000 | 0.5000
f[CL] | 1.0000 | 2.1152 | 1.1152 | 1.1152
f[V] | 15.0000 | 8.3532 | 0.4431 | 6.6468
f[BIO] | 0.8000 | 0.2407 | 0.6991 | 0.5593
f[LAG] | 1.0000 | 9.2028 | 8.2028 | 8.2028

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 5.0000 | 0.8936 | 0.8213 | 4.1064

### Compare Variance f[X]