:orphan: 





.. _direct_pd_tut:



Direct PD Model
###############

[Generated automatically as a Tutorial summary]

Inputs
******



Description
===========

:Name: direct_pd

:Title: Direct PD Model

:Author: Wright Dose Ltd

:Abstract: 

| A simple direct PD Model, based on the amount of drug in the body.
| The amount in the central compartment is determined by K, which has been previously estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; direct

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

:Diagram: 

Failed to create compartment diagram

True f[X] values
================

.. code-block:: pyml

    f[BASE] = 800.0000
    f[KOUT] = 0.0300
    f[ANOISE] = 0.5000



Starting f[X] values
====================

.. code-block:: pyml

    f[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 5.0000



Outputs
*******



Generating and Fitting Summaries
================================

* Gen: :ref:`direct_pd_gen` (gen)
* Fit: :ref:`direct_pd_fit` (fit)

Fitted f[X] values
==================

.. code-block:: pyml

    f[BASE] = 800.0443
    f[KOUT] = 0.0300
    f[ANOISE] = 10.0000



Plots
*****



Dense comp plots
================



.. thumbnail:: images/comp_dense/000001.svg
    :width: 200px


Alternatively see :ref:`direct_pd_dense_comp_plots`

Comparison
**********



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


.. code-block:: pyml

    -52.6006



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


.. code-block:: pyml

    460.7314



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]
=====================


No Variance f[X] values to compare.