:orphan: 





.. _blq_pk_norm_fit_fit:



Depot One Comp PK with BLQ observations set to LLQ
##################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: blq_pk_norm_fit

:Title: Depot One Comp PK with BLQ observations set to LLQ

:Author: J.R. Hartley

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations set to LLQ (lower limit of quantification).

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

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

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

:Diagram: 


.. thumbnail:: blq_pk_norm_fit.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

    122013.4799


which required 1.30 iterations and took 333.52 seconds

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

.. code-block:: pyml

    f[KA] = 0.8984
    f[CL] = 0.9553
    f[V1] = 88.4773
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0000, -0.0001, 0.0005 ],
        [ -0.0001, 0.0007, -0.0063 ],
        [ 0.0005, -0.0063, 0.0600 ],
    ]
    f[PNOISE] = 0.2339
    f[ANOISE] = 0.0100



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <blq_pk_norm_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:`blq_pk_norm_fit_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      1.0000          0.8984         0.1016        0.1016
f[CL]                      1.0000          0.9553         0.0447        0.0447
f[V1]                     20.0000         88.4773         3.4239       68.4773
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.2339         1.3392        0.1339
===============  ================  ==============  =============  ============

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         -0.0001         1.0057        0.0101
f[KA_isv;V1_isv]            0.0100          0.0005         0.9457        0.0095
f[CL_isv;KA_isv]            0.0100         -0.0001         1.0057        0.0101
f[CL_isv]                   0.0500          0.0007         0.9868        0.0493
f[CL_isv;V1_isv]            0.0100         -0.0063         1.6285        0.0163
f[V1_isv;KA_isv]            0.0100          0.0005         0.9457        0.0095
f[V1_isv;CL_isv]            0.0100         -0.0063         1.6285        0.0163
f[V1_isv]                   0.0500          0.0600         0.1997        0.0100
================  ================  ==============  =============  ============