:orphan: 





.. _blq_pk_norm_fit_ignore_fit:



Depot One Comp PK ignoring BLQ observations.
############################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: blq_pk_norm_fit_ignore

:Title: Depot One Comp PK ignoring BLQ observations.

:Author: J.R. Hartley

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations removed from data set.

:Keywords: tutorial; pk; advan4; dep_two_cmp; blq

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

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

:Diagram: 


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


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

.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100



Outputs
*******



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

.. code-block:: pyml

    -829.1270


which required 1.30 iterations and took 246.45 seconds

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

.. code-block:: pyml

    f[KA] = 0.2308
    f[CL] = 1.8474
    f[V1] = 52.2821
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0000, 0.0002, 0.0004 ],
        [ 0.0002, 0.0399, 0.0067 ],
        [ 0.0004, 0.0067, 0.0987 ],
    ]
    f[PNOISE] = 0.1491
    f[ANOISE] = 0.0100



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <blq_pk_norm_fit_ignore.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:`blq_pk_norm_fit_ignore_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      1.0000          0.2308         0.7692        0.7692
f[CL]                      1.0000          1.8474         0.8474        0.8474
f[V1]                     20.0000         52.2821         1.6141       32.2821
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.1491         0.4912        0.0491
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          0.0000         0.9999        0.0500
f[KA_isv;CL_isv]            0.0100          0.0002         0.9820        0.0098
f[KA_isv;V1_isv]            0.0100          0.0004         0.9641        0.0096
f[CL_isv;KA_isv]            0.0100          0.0002         0.9820        0.0098
f[CL_isv]                   0.0500          0.0399         0.2015        0.0101
f[CL_isv;V1_isv]            0.0100          0.0067         0.3270        0.0033
f[V1_isv;KA_isv]            0.0100          0.0004         0.9641        0.0096
f[V1_isv;CL_isv]            0.0100          0.0067         0.3270        0.0033
f[V1_isv]                   0.0500          0.0987         0.9748        0.0487
================  ================  ==============  =============  ============