Depot One Comp PK ignoring BLQ observations.¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk_norm_fit_ignore |
|---|---|
| Title: | Depot One Comp PK ignoring BLQ observations. |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification)
observations removed from data set.
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_norm_fit_ignore.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 0.2516 | 0.7484 | 0.7484 |
| f[CL] | 1.0000 | 1.8540 | 0.8540 | 0.8540 |
| f[V1] | 20.0000 | 53.2258 | 33.2258 | 1.6613 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.1494 | 0.0494 | 0.4944 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 0.0000 | 0.0500 | 0.9999 |
| f[KA_isv;CL_isv] | 0.0100 | 0.0001 | 0.0099 | 0.9907 |
| f[KA_isv;V1_isv] | 0.0100 | 0.0004 | 0.0096 | 0.9560 |
| f[CL_isv;KA_isv] | 0.0100 | 0.0001 | 0.0099 | 0.9907 |
| f[CL_isv] | 0.0500 | 0.0297 | 0.0203 | 0.4060 |
| f[CL_isv;V1_isv] | 0.0100 | -0.0018 | 0.0118 | 1.1756 |
| f[V1_isv;KA_isv] | 0.0100 | 0.0004 | 0.0096 | 0.9560 |
| f[V1_isv;CL_isv] | 0.0100 | -0.0018 | 0.0118 | 1.1756 |
| f[V1_isv] | 0.0500 | 0.1109 | 0.0609 | 1.2183 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.2516
f[CL] = 1.8540
f[V1] = 53.2258
f[KA_isv,CL_isv,V1_isv] = [
[ 0.0000, 0.0001, 0.0004 ],
[ 0.0001, 0.0297, -0.0018 ],
[ 0.0004, -0.0018, 0.1109 ],
]
f[PNOISE] = 0.1494
f[ANOISE] = 0.0100
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) |
| Likelihoods: | lx_params.csv (fit) |
Inputs¶
| Input Data: | synthetic_data.csv |
|---|
Starting f[X] values (before fitting)¶
f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[KA_isv,CL_isv,V1_isv] = [
[ 0.0500, 0.0100, 0.0100 ],
[ 0.0100, 0.0500, 0.0100 ],
[ 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000
f[ANOISE] = 0.0100