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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

-156.7244

which required 27 iterations and took 51.28 seconds

Final fitted fixed effects

f[KA] = 1.0000
f[CL] = 2.2932
f[V] = 24.0277
f[PNOISE_STD] = 0.4973
f[ANOISE_STD] = 0.0595
f[CL_isv] = 10.0000
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] 2.2932 1.0000 1.2932 1.2932
f[V] 24.0277 15.0000 0.6018 9.0277

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE_STD] 0.4973 0.2000 1.4866 0.2973
f[ANOISE_STD] 0.0595 0.2000 0.7025 0.1405

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[CL_isv] 10.0000 0.0100 999.0000 9.9900
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