:orphan: 





.. _blq_pk_tut:



Depot + One compartment PK with BLQ
###################################

[Generated automatically as a Tutorial summary]

Model Description
*****************


:Name: blq_pk

:Title: Depot + One compartment PK with BLQ

:Author: PoPy for PK/PD

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification) observations.

:Keywords: tutorial; pk; advan4; dep_two_cmp; blq

:Input Script: :download:`blq_pk_tut.pyml <blq_pk_tut.pyml>`

:Diagram: 


.. thumbnail:: compartment_diagram.svg
    :width: 200px


Comparison
**********



True objective value
====================


.. code-block:: pyml

    -781.3723



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


.. code-block:: pyml

    -786.5417



Compare Main f[X]
=================



.. csv-table:: 
    :file: fx_comp_main.csv
    :header-rows: 1


Compare Noise f[X]
==================



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


Compare Variance f[X]
=====================



.. csv-table:: 
    :file: fx_comp_variance.csv
    :header-rows: 1


Outputs
*******



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


.. code-block:: pyml

    f[KA] = 0.2060
    f[CL] = 1.9987
    f[V1] = 50.9527
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0532, 0.0111, -0.0286 ],
        [ 0.0111, 0.0289, 0.0239 ],
        [ -0.0286, 0.0239, 0.0642 ],
    ]
    f[PNOISE] = 0.1469
    f[ANOISE] = 0.0100



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`blq_pk_gen` (gen)
* Fit: :ref:`blq_pk_fit` (fit)

Inputs
******



True f[X] values (for simulation)
=================================

.. code-block:: pyml

    f[KA] = 0.2000
    f[CL] = 2.0000
    f[V1] = 50.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.1000, 0.0200, 0.0100 ],
        [ 0.0200, 0.0300, 0.0200 ],
        [ 0.0100, 0.0200, 0.0900 ],
    ]
    f[PNOISE] = 0.1500
    f[ANOISE] = 0.0100



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

.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100

