:orphan: 





.. _weight_covariate_gen:



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

[Generated automatically as a Generation 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_gen.pyml <weight_covariate_gen.pyml>`

:Diagram: 


.. thumbnail:: weight_covariate_gen.pyml_output/gen/compartment_diagram.svg
    :width: 200px


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

.. code-block:: pyml

    f[KA] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE] = 0.1
    f[ANOISE] = 0.05
    f[WT_EFFECT] = 0.75



Outputs
*******



Generated parameter .csv files
==============================


:Fixed Effects: :download:`fx_params.csv (gen) <weight_covariate_gen.pyml_output/gen/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (gen) <weight_covariate_gen.pyml_output/gen/rx_params.csv>`

:Model params: :download:`mx_params.csv (gen) <weight_covariate_gen.pyml_output/gen/mx_params.csv>`

:State values: :download:`sx_params.csv (gen) <weight_covariate_gen.pyml_output/gen/sx_params.csv>`

:Predictions: :download:`px_params.csv (gen) <weight_covariate_gen.pyml_output/gen/px_params.csv>`


:Synthetic Data: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Plots
*****



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



.. thumbnail:: images/gen_dense/000000.svg
    :width: 200px


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


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


Alternatively see :ref:`weight_covariate_dense_sim_plots`