# Bioavailability and Lag

[Generated automatically as a Fitting summary]

## Model Description

Name:

  biolag_abs_base

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_base_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 | 2.4714 | 1.4714 | 1.4714
f[V] | 15.0000 | 12.7971 | 2.2029 | 0.1469
f[BIO] | 0.5000 | 0.4497 | 0.0503 | 0.1007
f[LAG] | 1.0000 | 8.7958 | 7.7958 | 7.7958

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 5.0000 | 0.8946 | 4.1054 | 0.8211

### Compare Variance f[X]

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    38.8670

which required 1.18 iterations and took 12.78 seconds

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

    f[KA] = 1.0000
    f[CL] = 2.4714
    f[V] = 12.7971
    f[ANOISE_STD] = 0.8946
    f[BIO] = 0.4497
    f[LAG] = 8.7958

### 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] = 0.5000
    f[LAG] = 1.0000