Depot + One compartment PK with BLQ¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk |
|---|---|
| Title: | Depot + One compartment PK with BLQ |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification) observations.
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 0.1955 | 0.8045 | 0.8045 |
| f[CL] | 1.0000 | 2.0248 | 1.0248 | 1.0248 |
| f[V1] | 20.0000 | 46.9689 | 26.9689 | 1.3484 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.1389 | 0.0389 | 0.3888 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 0.1374 | 0.0874 | 1.7471 |
| f[KA_isv;CL_isv] | 0.0100 | 0.0156 | 0.0056 | 0.5555 |
| f[KA_isv;V1_isv] | 0.0100 | 0.0621 | 0.0521 | 5.2097 |
| f[CL_isv;KA_isv] | 0.0100 | 0.0156 | 0.0056 | 0.5555 |
| f[CL_isv] | 0.0500 | 0.0392 | 0.0108 | 0.2169 |
| f[CL_isv;V1_isv] | 0.0100 | 0.0199 | 0.0099 | 0.9866 |
| f[V1_isv;KA_isv] | 0.0100 | 0.0621 | 0.0521 | 5.2097 |
| f[V1_isv;CL_isv] | 0.0100 | 0.0199 | 0.0099 | 0.9866 |
| f[V1_isv] | 0.0500 | 0.1255 | 0.0755 | 1.5110 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.1955
f[CL] = 2.0248
f[V1] = 46.9689
f[KA_isv,CL_isv,V1_isv] = [
[ 0.1374, 0.0156, 0.0621 ],
[ 0.0156, 0.0392, 0.0199 ],
[ 0.0621, 0.0199, 0.1255 ],
]
f[PNOISE] = 0.1389
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: | cx_obs_params.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