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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:dep_one_cmp_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:dep_one_cmp_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

Fitted f[X] values

f[KA] = 1.0000
f[CL] = 2.4143
f[V] = 28.3578
f[PNOISE_STD] = 0.5960
f[ANOISE_STD] = 0.1180
f[CL_isv] = 0.0000

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

2777.3506

Final fitted objective value

-144.9258

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 0.5 1 0.3 233.33% 7.00e-01
f[CL] 1 2.41 3 19.52% 5.86e-01
f[V] 15 28.4 20 41.79% 8.36e+00

Compare Noise f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[PNOISE_STD] 0.2 0.596 0.1 496.03% 4.96e-01
f[ANOISE_STD] 0.2 0.118 0.05 136.08% 6.80e-02

Compare Variance f[X]

No Variance f[X] values to compare.

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