:orphan: 





.. _weight_covariate_tut:



Body Weight Covariate
#####################

[Generated automatically as a Tutorial summary]

Inputs
******



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

:Name: weight_covariate

:Title: Body Weight Covariate

:Author: Wright Dose Ltd

:Abstract: 

| One compartment model with absorption compartment and CL/V parametrisation.
| There are no random effects here. Each individual just has a different weight.
| The weight is a covariate for the m[CL] clearance parameter for each individual.
| Only the f[WT_EFFECT] and f[V] fixed effect parameters are estimated, other f[X] are fixed.

:Keywords: one compartment model; dep_one_cmp_cl; weight; covariate effect

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

:Diagram: 

Failed to create compartment diagram

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

.. code-block:: pyml

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 0.7500



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

.. code-block:: pyml

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 15.0000
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 1.0000



Outputs
*******



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

* Gen: :ref:`weight_covariate_gen` (gen)
* Fit: :ref:`weight_covariate_fit` (fit)

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

.. code-block:: pyml

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.2610
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 0.6657



Plots
*****



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



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


.. thumbnail:: images/comp_dense/000002.svg
    :width: 200px


.. thumbnail:: images/comp_dense/000003.svg
    :width: 200px


Alternatively see :ref:`weight_covariate_dense_comp_plots`

Comparison
**********



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


.. code-block:: pyml

    -483.3718



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


.. code-block:: pyml

    -486.0798



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



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


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


No Noise f[X] values to compare.

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


No Variance f[X] values to compare.