:orphan: 





.. _biolag_abs_both_fit:



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: :download:`biolag_abs_both_fit.pyml <biolag_abs_both_fit.pyml>`

:Diagram: 


.. thumbnail:: biolag_abs_both_fit.pyml_output/compartment_diagram.svg
    :width: 200px


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
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

Outputs
*******



Final objective value
=====================

.. code-block:: pyml

    38.7572


which required 1.29 iterations and took 13.14 seconds

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


.. code-block:: pyml

    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: :download:`fx_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <biolag_abs_both_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`cx_obs_params.csv <cx_obs_params.csv>`


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


.. code-block:: pyml

    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

