Simple Fit Example¶
[Generated automatically as a Fitting summary]
Model Description¶
Name: | fit_example1 |
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Title: | Simple Fit Example |
Author: | PoPy for PK/PD |
Abstract: |
One compartment model with elimination rate constant KE.
Keywords: | one compartment model; iv_one_cmp_k |
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Input Script: | fit_example1.pyml |
Diagram: |
Comparison¶
Compare Main f[X]¶
Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
---|---|---|---|---|
f[KE] | 0.0500 | 0.0968 | 0.0468 | 0.9356 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.2135 | 0.1135 | 1.1351 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
---|---|---|---|---|
f[KE_isv] | 0.1000 | 0.0160 | 0.0840 | 0.8399 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Population simulated (msim) plots¶
allOBS_vs_TIME_VPC |
Outputs¶
Fitted f[X] values (after fitting)¶
f[KE] = 0.0968
f[PNOISE] = 0.2135
f[KE_isv] = 0.0160
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: | fit_example1_data.csv |
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Starting f[X] values (before fitting)¶
f[KE] = 0.0500
f[PNOISE] = 0.1000
f[KE_isv] = 0.1000