![[Stable]](figures/lifecycle-stable.svg)
Generates an data.frame with subject-specific covariate data from an NPAG or IT2B object
Value
The output of makeCov is a dataframe of class PMcov,
which has the following columns:
- id
Subject identification
- time
Times of covariate observations
- covnames...
Columns with each covariate observations in the dataset for each subject and
time- parnames...
Columns with each parameter in the model and the
icensummary for each subject, replicated as necessary for covariate observation times and duplicated for Bayesian parameter means and medians- icen
The type of summarized Bayesian posterior individual parameter values: mean or median.
Details
For each subject, makeCov extracts covariate information and Bayesian posterior parameter estimates.
This output of this function is suitable for exploration of covariate-parameter, covariate-time, or parameter-time relationships.
Examples
library(PmetricsData)
cov <- makeCov(NPex$NPdata)
cov
#> id time wt africa age gender height Ka Ke V Tlag1
#> 1 1 0 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 2 1 24 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 3 1 48 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 4 1 72 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 5 1 96 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 6 1 120 46.7 1 21 1 160 0.440192 0.0246167 66.3882 0.5544030
#> 7 2 0 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 8 2 24 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 9 2 48 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 10 2 72 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 11 2 96 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 12 2 120 66.5 1 30 1 174 0.738613 0.0398178 119.4690 0.0259495
#> 13 3 0 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 14 3 24 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 15 3 48 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 16 3 72 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 17 3 96 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 18 3 120 46.7 1 24 0 164 0.899944 0.0431027 108.6490 2.0959200
#> 19 4 0 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 20 4 24 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 21 4 48 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 22 4 72 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 23 4 96 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 24 4 120 50.8 1 25 1 165 0.897550 0.0564307 119.8190 0.6883040
#> 25 5 0 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 26 5 24 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 27 5 48 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 28 5 72 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 29 5 96 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 30 5 120 65.8 1 22 1 181 0.105273 0.0675058 113.3430 0.0186591
#> 31 6 0 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 32 6 24 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 33 6 48 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 34 6 72 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 35 6 96 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 36 6 120 65.0 1 23 1 177 0.895237 0.0348831 71.8617 1.9978800
#> 37 7 0 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 38 7 24 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 39 7 48 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 40 7 72 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 41 7 96 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 42 7 120 51.7 1 27 0 161 0.215198 0.0832836 35.2243 1.7966900
#> 43 8 0 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 44 8 24 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 45 8 48 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 46 8 72 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 47 8 96 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 48 8 120 51.2 1 22 1 163 0.895494 0.0348882 71.8466 1.9985200
#> 49 9 0 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 50 9 24 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 51 9 48 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 52 9 72 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 53 9 96 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 54 9 120 55.0 1 23 1 174 0.790042 0.0439417 101.7520 0.8801720
#> 55 10 0 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 56 10 24 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 57 10 48 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 58 10 72 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 59 10 96 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 60 10 120 52.1 1 32 1 163 0.655770 0.0615898 61.6882 0.8013670
#> 61 11 0 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 62 11 24 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 63 11 48 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 64 11 72 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 65 11 96 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 66 11 120 56.5 1 34 1 165 0.583214 0.0683228 73.1084 1.3385500
#> 67 12 0 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 68 12 24 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 69 12 48 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 70 12 72 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 71 12 96 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 72 12 120 47.9 1 54 0 160 0.470094 0.0306855 91.8654 1.0252500
#> 73 13 0 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 74 13 24 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 75 13 48 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 76 13 72 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 77 13 96 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 78 13 120 60.5 1 24 1 180 0.215198 0.0832837 35.2243 1.7957700
#> 79 14 0 59.2 1 26 1 174 0.555242 0.0439039 117.8380 0.2868650
#> 80 14 24 59.2 1 26 1 174 0.555242 0.0439039 117.8380 0.2868650
#> 81 14 48 59.2 1 26 1 174 0.555242 0.0439039 117.8380 0.2868650
#> 82 14 72 59.2 1 26 1 174 0.555242 0.0439039 117.8380 0.2868650
#> 83 14 96 59.2 1 26 1 174 0.555242 0.0439039 117.8380 0.2868650
#> icen
#> 1 mean
#> 2 mean
#> 3 mean
#> 4 mean
#> 5 mean
#> 6 mean
#> 7 mean
#> 8 mean
#> 9 mean
#> 10 mean
#> 11 mean
#> 12 mean
#> 13 mean
#> 14 mean
#> 15 mean
#> 16 mean
#> 17 mean
#> 18 mean
#> 19 mean
#> 20 mean
#> 21 mean
#> 22 mean
#> 23 mean
#> 24 mean
#> 25 mean
#> 26 mean
#> 27 mean
#> 28 mean
#> 29 mean
#> 30 mean
#> 31 mean
#> 32 mean
#> 33 mean
#> 34 mean
#> 35 mean
#> 36 mean
#> 37 mean
#> 38 mean
#> 39 mean
#> 40 mean
#> 41 mean
#> 42 mean
#> 43 mean
#> 44 mean
#> 45 mean
#> 46 mean
#> 47 mean
#> 48 mean
#> 49 mean
#> 50 mean
#> 51 mean
#> 52 mean
#> 53 mean
#> 54 mean
#> 55 mean
#> 56 mean
#> 57 mean
#> 58 mean
#> 59 mean
#> 60 mean
#> 61 mean
#> 62 mean
#> 63 mean
#> 64 mean
#> 65 mean
#> 66 mean
#> 67 mean
#> 68 mean
#> 69 mean
#> 70 mean
#> 71 mean
#> 72 mean
#> 73 mean
#> 74 mean
#> 75 mean
#> 76 mean
#> 77 mean
#> 78 mean
#> 79 mean
#> 80 mean
#> 81 mean
#> 82 mean
#> 83 mean
#> [ reached 'max' / getOption("max.print") -- omitted 157 rows ]
names(cov)
#> [1] "id" "time" "wt" "africa" "age" "gender" "height" "Ka"
#> [9] "Ke" "V" "Tlag1" "icen"