:orphan: 





.. _dep_one_cmp_cl_iov_naive_fit:



One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0
################################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: dep_one_cmp_cl_iov_naive

:Title: One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance
| Here f[CL_iov] is not estimated it is set to zero.

:Keywords: one compartment model; dep_one_cmp_cl; iov

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

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

:Diagram: 


.. thumbnail:: dep_one_cmp_cl_iov_naive_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] = 0.0000



Outputs
*******



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

.. code-block:: pyml

    -190.3438


which required N. iterations and took 254.12 seconds

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

.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 2.2628
    f[V] = 24.1247
    f[PNOISE_STD] = 0.4635
    f[ANOISE_STD] = 0.0615
    f[CL_isv] = 0.1294
    f[CL_iov] = 0.0000



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <dep_one_cmp_cl_iov_naive_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_naive_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]                    2.2628            1.0000         1.2628        1.2628
f[V]                    24.1247           15.0000         0.6083        9.1247
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]            0.4635            0.2000         1.3177        0.2635
f[ANOISE_STD]            0.0615            0.2000         0.6926        0.1385
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]                0.1294            0.0100        11.9434        0.1194
===============  ==============  ================  =============  ============