Diagonal matrix generation diagonal matrix fit using separate univariate normals¶

[Generated automatically as a Fitting summary]

Inputs¶

Description¶

Name:	gen_indep_fit_indep
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
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 objective value¶

-2171.9050

which required N. iterations and took 442.46 seconds

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] = 0.1719
f[V_isv] = 0.0879

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)`