:orphan: 





.. _dep_one_cmp_cl_iov_fit:



One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2
###############################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: dep_one_cmp_cl_iov

:Title: One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance

:Keywords: one compartment model; dep_one_cmp_cl; iov

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

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

:Diagram: 


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


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

.. code-block:: pyml

    f[KA] = 0.5000
    f[CL] = 1.0000
    f[V] = 15.0000
    f[PNOISE_STD] = 0.2000
    f[ANOISE_STD] = 0.2000
    f[CL_isv] = 0.0100
    f[CL_iov] = 0.0100



Outputs
*******



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

.. code-block:: pyml

    -275.4309


which required 33 iterations and took 92.42 seconds

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

.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 2.1808
    f[V] = 19.7903
    f[PNOISE_STD] = 0.2151
    f[ANOISE_STD] = 0.0508
    f[CL_isv] = 0.0741
    f[CL_iov] = 0.1014



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <dep_one_cmp_cl_iov_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:`dep_one_cmp_cl_iov_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                    1.0000            0.5000         1.0000        0.5000
f[CL]                    2.1808            1.0000         1.1808        1.1808
f[V]                    19.7903           15.0000         0.3194        4.7903
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]            0.2151            0.2000         0.0755        0.0151
f[ANOISE_STD]            0.0508            0.2000         0.7461        0.1492
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]                0.0741            0.0100         6.4118        0.0641
f[CL_iov]                0.1014            0.0100         9.1392        0.0914
===============  ==============  ================  =============  ============