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One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0¶

[Generated automatically as a Tutorial summary]

Inputs¶

Description¶

Name:	d1cmp_cl_isv_naive
Title:	One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0
Author:	Wright Dose Ltd
Abstract:

Population one Compartment Model with Absorption and Inter-subject Variance

Here f[CL_isv] is not estimated it is set to zero.

Keywords:	one compartment model; dep_one_cmp_cl
Input Script:	`d1cmp_cl_isv_naive.pyml`
Diagram:

True f[X] values¶

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv] = 0.2000

Starting f[X] values¶

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] = 0.0000

Outputs¶

Generated data .csv file¶

Synthetic Data:	`synthetic_data.csv`

Generating and Fitting Summaries¶

Fitted f[X] values¶

f[KA] = 0.2081
f[CL] = 3.0966
f[V] = 14.7707
f[PNOISE_STD] = 0.3766
f[ANOISE_STD] = 0.1619
f[CL_isv] = 0.0000

Plots¶

Dense comp plots¶

Alternatively see All dense_comp graph plots

Comparison¶

True objective value¶

787.9714

Final fitted objective value¶

-200.9397

Compare Main f[X]¶

Name	Initial	Fitted	True	Prop. Error	Abs. Error
f[KA]	0.5	0.208	0.3	30.64%	9.19e-02
f[CL]	1	3.1	3	3.22%	9.66e-02
f[V]	15	14.8	20	26.15%	5.23e+00

Compare Noise f[X]¶

Name	Initial	Fitted	True	Prop. Error	Abs. Error
f[PNOISE_STD]	0.2	0.377	0.1	276.64%	2.77e-01
f[ANOISE_STD]	0.2	0.162	0.05	223.71%	1.12e-01

Compare Variance f[X]¶

No Variance f[X] values to compare.

Documentation