:orphan: 





.. _weight_covariate_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: weight_covariate

:Title: Body Weight Covariate

:Author: PoPy for PK/PD

: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>`

:Diagram: 


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


Comparison
**********



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


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

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




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




Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`weight_covariate_simulated_sim_plots`

Population simulated (sim) plots
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS_vs_TIME/comb_spag.svg
            :width: 200px
      - allOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    -486.0798


which required 1.6 iterations and took 11.74 seconds

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


.. 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/solN/fx_params.csv>`

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <weight_covariate_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


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


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


.. 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

