# Bioavailability and Lag

[Generated automatically as a Fitting summary]

## Model Description

Name:

  biolag_abs_both

Title:

  Bioavailability and Lag

Author:

  PoPy for PK/PD

Abstract:

One compartment model absorption dosing with bioavailability and lag parameters.

Keywords:

  identifiability; bioavailability; lag

Input Script:

  `biolag_abs_both_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 0.5000 | 1.0000 | 0.5000 | 1.0000
f[CL] | 1.0000 | 3.5664 | 2.5664 | 2.5664
f[V] | 15.0000 | 21.8447 | 6.8447 | 0.4563
f[BIO] | 0.8000 | 0.6829 | 0.1171 | 0.1464
f[LAG] | 1.0000 | 8.9078 | 7.9078 | 7.9078

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 5.0000 | 0.8937 | 4.1063 | 0.8213

### Compare Variance f[X]

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    38.7572

which required 1.29 iterations and took 13.14 seconds

### Fitted f[X] values (after fitting)

    f[KA] = 1.0000
    f[CL] = 3.5664
    f[V] = 21.8447
    f[ANOISE_STD] = 0.8937
    f[BIO] = 0.6829
    f[LAG] = 8.9078

### 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)`

Likelihoods:

  `lx_params.csv (fit)`

## Inputs

Input Data:

  `cx_obs_params.csv`

### Starting f[X] values (before fitting)

    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