:orphan: 





.. _inf_rate_dosing_repeated_fit:



Repeated Infusion Rate Dose with first order elimination.
#########################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: inf_rate_dosing_repeated

:Title: Repeated Infusion Rate Dose with first order elimination.

:Author: Wright Dose Ltd

:Abstract: 

| Repeated infusion rate dose into single compartment with first order elimination.

:Keywords: inf_rate; repeated; advan1; iv_one_cmp_cl; one compartment model

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

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

:Diagram: 


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


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

(Model has no fixed effects)

Outputs
*******



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

.. code-block:: pyml

    nan


which required 3 iterations and took 0.08 seconds

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

(Model has no fixed effects)

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


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

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

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

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

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



Plots
*****



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



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


Alternatively see :ref:`inf_rate_dosing_repeated_dense_sim_plots`

Comparison
**********



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




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




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


