:orphan: 





.. _linear_pd_pop_tut:



Linear PD model
###############

[Generated automatically as a Tutorial summary]

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


:Name: linear_pd_pop

:Title: Linear PD model

:Author: PoPy for PK/PD

:Abstract: 

| A simple (i.e. no PD compartments) Linear PD Model.
| Model consists of a baseline which increases linearly with concentration.

:Keywords: pd; linear; one compartment model

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    185.2839



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


.. code-block:: pyml

    178.3795



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[BL] = 9.8634
    f[SLOPE] = 1.0393
    f[PNOISE] = 0.0148
    f[ANOISE] = 0.4364
    f[BL_isv,SLOPE_isv] = [
        [ 0.0537, 0.0062 ],
        [ 0.0062, 0.0403 ],
    ]



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


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


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


* Gen: :ref:`linear_pd_pop_gen` (gen)
* Fit: :ref:`linear_pd_pop_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[BL] = 10.0000
    f[SLOPE] = 1.0000
    f[PNOISE] = 0.0100
    f[ANOISE] = 0.5000
    f[BL_isv,SLOPE_isv] = [
        [ 0.0500, 0.0000 ],
        [ 0.0000, 0.0300 ],
    ]



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

.. code-block:: pyml

    f[BL] = 15.0000
    f[SLOPE] = 0.5000
    f[PNOISE] = 0.0300
    f[ANOISE] = 0.2000
    f[BL_isv,SLOPE_isv] = [
        [ 0.1000, 0.0000 ],
        [ 0.0000, 0.0500 ],
    ]

