- Language: en
Full matrix generation diagonal matrix fit¶
[Generated automatically as a Fitting summary]
Model Description¶
Name: | gen_full_fit_diag |
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Title: | Full matrix generation diagonal matrix fit |
Author: | PoPy for PK/PD |
Abstract: |
One compartment model with absorption compartment and CL/V parametrisation.
This script uses a full covariance matrix to generate the data, but a diagonal matrix to fit.
Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix |
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Input Script: | gen_full_fit_diag_fit.pyml |
Diagram: |
Comparison¶
Compare Main f[X]¶
Compare Noise f[X]¶
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
---|---|---|---|---|
f[CL_isv] | 0.0100 | 0.1280 | 0.1180 | 11.8005 |
f[CL_isv;V_isv] | 0.0000 | 0.0000 | 0.0000 | INF |
f[V_isv;CL_isv] | 0.0000 | 0.0000 | 0.0000 | INF |
f[V_isv] | 0.0100 | 0.1581 | 0.1481 | 14.8101 |
Population simulated (sim) plots¶
allOBS_vs_TIME |
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
[ 0.1280, 0.0000 ],
[ 0.0000, 0.1581 ],
]
Fitted parameter .csv files¶
Fixed Effects: | fx_params.csv (fit) |
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Random Effects: | rx_params.csv (fit) |
Model params: | mx_params.csv (fit) |
State values: | sx_params.csv (fit) |
Predictions: | px_params.csv (fit) |
Likelihoods: | lx_params.csv (fit) |
Inputs¶
Input Data: | cx_obs_params.csv |
---|
Starting f[X] values (before fitting)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
[ 0.0100, 0.0000 ],
[ 0.0000, 0.0100 ],
]