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One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

[Generated automatically as a Fitting summary]

Inputs

Description

Name:dep_one_cmp_cl_iov_05
Title:One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5
Author:Wright Dose Ltd
Abstract:
Population one Compartment Model with Absorption and Inter-occasion Variance
Here f[CL_isv] true value is 0.5
Keywords:one compartment model; dep_one_cmp_cl; iov
Input Script:dep_one_cmp_cl_iov_05_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

-276.6985

which required N. iterations and took 256.08 seconds

Final fitted fixed effects

f[KA] = 1.0000
f[CL] = 1.9217
f[V] = 20.2630
f[PNOISE_STD] = 0.2081
f[ANOISE_STD] = 0.0480
f[CL_isv] = 0.2662
f[CL_iov] = 0.0084

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] 1.9217 1.0000 0.9217 0.9217
f[V] 20.2630 15.0000 0.3509 5.2630

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE_STD] 0.2081 0.2000 0.0405 0.0081
f[ANOISE_STD] 0.0480 0.2000 0.7599 0.1520

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[CL_isv] 0.2662 0.0100 25.6151 0.2562
f[CL_iov] 0.0084 0.0100 0.1550 0.0016
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