• Language: en

One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

[Generated automatically as a Fitting 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_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] = 0.0000

Outputs

Final objective value

-144.9258

which required N. iterations and took 89.12 seconds

Final fitted fixed effects

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

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] 2.4143 1.0000 1.4143 1.4143
f[V] 28.3578 15.0000 0.8905 13.3578

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE_STD] 0.5960 0.2000 1.9802 0.3960
f[ANOISE_STD] 0.1180 0.2000 0.4098 0.0820

Compare Variance f[X]

Back to Top