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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:dep_one_cmp_cl_iov_naive_fit.pyml
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

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

-190.0814

which required N. iterations and took 177.31 seconds

Final fitted fixed effects

f[KA] = 1.0000
f[CL] = 2.2395
f[V] = 24.3910
f[PNOISE_STD] = 0.4529
f[ANOISE_STD] = 0.0599
f[CL_isv] = 0.1276
f[CL_iov] = 0.0000

Fitted parameter .csv files

Fixed Effects:fx_params.csv (fit)
Random Effects:rx_params.csv (fit)
Model params:mx_params.csv (fit)
State values:sx_params.csv (fit)
Predictions:px_params.csv (fit)

Plots

Dense sim plots

Alternatively see All dense_sim graph 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] 2.2395 1.0000 1.2395 1.2395
f[V] 24.3910 15.0000 0.6261 9.3910

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE_STD] 0.4529 0.2000 1.2646 0.2529
f[ANOISE_STD] 0.0599 0.2000 0.7004 0.1401

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[CL_isv] 0.1276 0.0100 11.7552 0.1176
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