Depot One Comp PK with BLQ observations set to LLQ¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk_norm_fit |
|---|---|
| Title: | Depot One Comp PK with BLQ observations set to LLQ |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification)
observations set to LLQ (lower limit of quantification).
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_norm_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 2.5908 | 1.5908 | 1.5908 |
| f[CL] | 1.0000 | 0.9559 | 0.0441 | 0.0441 |
| f[V1] | 20.0000 | 86.1210 | 66.1210 | 3.3060 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.2288 | 0.1288 | 1.2877 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 1.1061 | 1.0561 | 21.1229 |
| f[KA_isv;CL_isv] | 0.0100 | -0.0133 | 0.0233 | 2.3297 |
| f[KA_isv;V1_isv] | 0.0100 | 0.2332 | 0.2232 | 22.3169 |
| f[CL_isv;KA_isv] | 0.0100 | -0.0133 | 0.0233 | 2.3297 |
| f[CL_isv] | 0.0500 | 0.0002 | 0.0498 | 0.9967 |
| f[CL_isv;V1_isv] | 0.0100 | -0.0028 | 0.0128 | 1.2814 |
| f[V1_isv;KA_isv] | 0.0100 | 0.2332 | 0.2232 | 22.3169 |
| f[V1_isv;CL_isv] | 0.0100 | -0.0028 | 0.0128 | 1.2814 |
| f[V1_isv] | 0.0500 | 0.0506 | 0.0006 | 0.0113 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 2.5908
f[CL] = 0.9559
f[V1] = 86.1210
f[KA_isv,CL_isv,V1_isv] = [
[ 1.1061, -0.0133, 0.2332 ],
[ -0.0133, 0.0002, -0.0028 ],
[ 0.2332, -0.0028, 0.0506 ],
]
f[PNOISE] = 0.2288
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