- Language: en
Diagonal matrix generation diagonal matrix fit using separate univariate normals¶
[Generated automatically as a Fitting summary]
Inputs¶
Description¶
Name: | gen_indep_fit_indep |
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Title: | Diagonal matrix generation diagonal matrix fit using separate univariate normals |
Author: | Wright Dose Ltd |
Abstract: |
One compartment model with absorption compartment and CL/V parametrisation.
This script uses a diagonal covariance matrix to generate the data and a diagonal covariance matrix to fit.
Note here the ‘diagonal matrix’ is implemented as two separate univariate normal distributions, which is equivalent.
Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix |
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Input Script: | gen_indep_fit_indep_fit.pyml |
Input Data: | synthetic_data.csv |
Diagram: |
Initial fixed effect estimates¶
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] = 0.0100
f[V_isv] = 0.0100
Outputs¶
Final fitted fixed effects¶
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] = 0.1972
f[V_isv] = 0.1174
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) |