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One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

[Generated automatically as a Fitting summary]

Inputs

Description

Name:d1cmp_cl_iov
Title:One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2
Author:Wright Dose Ltd
Abstract:
Population one Compartment Model with Absorption and Inter-occasion Variance
Keywords:one compartment model; dep_one_cmp_cl; iov
Input Script:d1cmp_cl_iov_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] = 0.0100

Outputs

Final objective value

-363.8118

which required 1.19 iterations and took 423.37 seconds

Final fitted fixed effects

f[KA] = 0.3345
f[CL] = 2.5856
f[V] = 20.2051
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0479
f[CL_isv] = 0.1199
f[CL_iov] = 0.0783

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 Starting Value Fitted Value Prop Change Abs Change
f[KA] 0.5000 0.3345 0.3309 0.1655
f[CL] 1.0000 2.5856 1.5856 1.5856
f[V] 15.0000 20.2051 0.3470 5.2051

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE_STD] 0.2000 0.1000 0.4998 0.1000
f[ANOISE_STD] 0.2000 0.0479 0.7605 0.1521

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[CL_isv] 0.0100 0.1199 10.9948 0.1099
f[CL_iov] 0.0100 0.0783 6.8312 0.0683
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