# Bioavailability and Lag

[Generated automatically as a Fitting summary]

## Model Description

Name:

  biolag_abs_lag

Title:

  Bioavailability and Lag

Author:

  PoPy for PK/PD

Abstract:

One compartment model absorption dosing with bioavailability and lag parameters.

Keywords:

  identifiability; bioavailability; lag; dep_one_cmp_cl

Input Script:

  `biolag_abs_lag_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 0.5000 | 0.9839 | 0.4839 | 0.9677
f[CL] | 1.0000 | 0.8818 | 0.1182 | 0.1182
f[V] | 15.0000 | 6.2090 | 8.7910 | 0.5861
f[BIO] | 0.8000 | 0.2744 | 0.5256 | 0.6570
f[LAG] | 1.0000 | 8.6439 | 7.6439 | 7.6439

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 5.0000 | 0.8927 | 4.1073 | 0.8215

### Compare Variance f[X]

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    38.5454

which required 1.30 iterations and took 11.44 seconds

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

    f[KA] = 0.9839
    f[CL] = 0.8818
    f[V] = 6.2090
    f[ANOISE_STD] = 0.8927
    f[BIO] = 0.2744
    f[LAG] = 8.6439

### 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