:orphan: 





.. _weight_covariate_fit:



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

[Generated automatically as a Fitting 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_fit.pyml <weight_covariate_fit.pyml>`

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

:Diagram: 


.. thumbnail:: weight_covariate_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. 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
*******



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

.. code-block:: pyml

    -486.0798


which required 1.6 iterations and took 3.59 seconds

Final fitted fixed effects
==========================

.. 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



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <weight_covariate_fit.pyml_output/fit/solN/fx_params.csv>`

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

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

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

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



Plots
*****



Dense sim plots
===============



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


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


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


Alternatively see :ref:`weight_covariate_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[V]                      15.0000         20.2610         0.3507        5.2610
f[WT_EFFECT]               1.0000          0.6657         0.3343        0.3343
===============  ================  ==============  =============  ============

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




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


