![[Stable]](figures/lifecycle-stable.svg)
Extracts final cycle information from NPAG or IT2B run.
Value
The output of makeFinal is a list of class PMfinal, which contains the following:
- popPoints
(NPAG only) Dataframe of the final cycle joint population density of grid points with column names equal to the name of each random parameter plus prob for the associated probability of that point
- popMean
The final cycle mean for each random parameter distribution
- popSD
The final cycle standard deviation for each random parameter distribution
- popCV
The final cycle coefficient of variation (SD/Mean) for each random parameter distribution
- popVar
The final cycle variance for each random parameter distribution
- popCov
The final cycle random parameter covariance matrix
- popCor
The final cycle random parameter correlation matrix
- popMedian
The final cycle median values for each random parameter
- postPoints
(NPAG only) Dataframe of posterior population points for each of the first 100 subject, with columns id, point, parameters and probability. The first column is the subject, the second column has the population point number, followed by the values for the parameters in that point and the probability.
- postMean
A nsub x npar data frame containing the means of the posterior distributions for each parameter.
- postSD
A nsub x npar data frame containing the SDs of the posterior distributions for each parameter.
- postVar
A nsub x npar data frame containing the variances of the posterior distributions for each parameter.
- postCov
NPAG only: An array of dimensions npar x npar x nsub that contains the covariances of the posterior distributions for each parameter and subject.
- postCor
NPAG only: An array of dimensions npar x npar x nsub that contains the correlations of the posterior distributions for each parameter and subject.
- postMed
A nsub x npar data frame containing the medians of the posterior distributions for each parameter.
- shrinkage
A data frame with the shrinkage for each parameter. The total population variance for a parameter is comprised of variance(EBE) plus average variance(EBD), where each subject's EBE is the Empirical Bayes Estimate or mean posterior value for the parameter. EBD is the Empirical Bayes Distribution, or the full Bayesian posterior parameter value distribution for each subject.
The typical definition of \(\eta\) shrinkage is \([1 - \frac{SD(\eta)}{\omega}]\) or \([1 - \frac{var(\eta)}{\omega^2}]\), where \(\eta\) is the EBE and \(\omega^2\) is the population variance of \(\eta\).
In parametric modeling approaches \(\eta\) is the interindividual variability around the typical (mean) value of the parameter in the population, usually referred to as \(\theta\). In nonparametric approaches, there is no assumption of normality, so \(\eta\) simply becomes each subject's mean parameter value estimate.
Here is how Pmetrics derives and then calculates shrinkage for a given parameter. $$popVar = var(EBE) + mean(var(EBD))$$ $$1 = \frac{var(EBE)}{popVar} + \frac{mean(var(EBD)}{popVar}$$ $$1 - \frac{var(EBE)}{popVar} = \frac{mean(var(EBD))}{popVar}$$ $$shrinkage = \frac{mean(var(EBD))}{popVar}$$ Shrinkage is therefore a fraction between 0 and 1. If Bayesian posterior distributions are wide for a given parameter and \(mean(var(EBD))\) is high due to sparse or uninformative sampling, then most of the population variance is due to this variance and shrinkage is high, i.e., individual posterior estimates (EBE) shrink towards the population mean. Be aware, however, that a Bayesian posterior parameter value distribution for a given subject who is sparsely sampled may also be a single support point with no variance. Therefore EBD under nonparametric assumptions is not always large with uninformative sampling. This means that shrinkage is not as readily interpretable in nonparametric population modeling.
An alternative is to consider the number of support points relative to the number of subjects. Highly informed, distinct subjects will result in the maximum possible number of support points, N, which is the same as the number of subjects. In contrast, badly undersampled subjects can result in only one support point. There is no formal criterion for this statistic, but it can be used in combination with shrinkage to assess the information content of the data.
- gridpts
(NPAG only) Initial number of support points
- nsub
Number of subjects
- ab
Matrix of boundaries for random parameter values
A plot method exists in plot for PMfinal objects.
Details
This function will parse the output of NPparse or ITparse to generate a
list suitable for analysis and plotting of NPAG or IT2B final cycle population values.
Examples
library(PmetricsData)
final <- makeFinal(NPex$NPdata)
final
#> $popPoints
#> Ka Ke V lag prob
#> 1 0.8999440 0.04310272 108.64920 2.09592004 0.05000771
#> 2 0.2151984 0.08328365 35.22432 1.80379203 0.10419106
#> 3 0.7927824 0.04393907 101.57052 0.88691202 0.04985577
#> 4 0.6552048 0.06166284 61.52304 0.80102401 0.04980239
#> 5 0.6575952 0.06211706 30.98496 1.92417603 0.04999970
#> 6 0.8952615 0.02318994 75.94093 1.76207524 0.05076679
#> 7 0.2151984 0.08328365 35.22432 1.77879203 0.04581144
#> 8 0.4324662 0.02464088 66.22597 0.53389922 0.04923321
#> 9 0.8976352 0.05642951 119.81946 0.68837602 0.05021143
#> 10 0.7540983 0.03531704 89.66107 0.68665921 0.05077266
#> 11 0.8008512 0.03427034 71.83326 1.04925602 0.04863507
#> 12 0.7477824 0.03960782 119.57052 0.01191201 0.04798899
#> 13 0.5527824 0.04393907 117.88302 0.28691201 0.05234436
#> 14 0.5829040 0.06831802 73.11720 1.33872003 0.05074312
#> 15 0.8963072 0.07349612 63.15906 1.17733602 0.04925686
#> 16 0.4608512 0.03055784 92.08326 1.02425602 0.04980933
#> 17 0.8958512 0.03488909 71.83326 1.99925603 0.10078061
#> 18 0.1018374 0.06755382 113.31523 0.01575520 0.04978951
#>
#> $popMean
#> Ka Ke V lag
#> 1 0.6282269 0.05138859 77.7699 1.185559
#>
#> $popSD
#> Ka Ke V lag
#> 1 0.26297 0.01960317 28.82353 0.6677845
#>
#> $popCV
#> Ka Ke V lag
#> 1 41.85908 38.14694 37.06258 56.32656
#>
#> $popVar
#> Ka Ke V lag
#> 1 0.06915324 0.0003842845 830.7959 0.4459361
#>
#> $popCov
#> Ka Ke V lag
#> Ka 0.069153244 -0.0029031355 2.5298201 0.026000528
#> Ke -0.002903136 0.0003842845 -0.2704453 0.002291387
#> V 2.529820102 -0.2704453298 830.7959212 -12.200365241
#> lag 0.026000528 0.0022913867 -12.2003652 0.445936089
#>
#> $popCor
#> Ka Ke V lag
#> Ka 1.0000000 -0.5631637 0.3337615 0.1480606
#> Ke -0.5631637 1.0000000 -0.4786366 0.1750393
#> V 0.3337615 -0.4786366 1.0000000 -0.6338542
#> lag 0.1480606 0.1750393 -0.6338542 1.0000000
#>
#> $popMedian
#> Ka Ke V lag
#> 1 0.7026888 0.04393907 72.47523 1.113296
#>
#> $popRanFix
#> NULL
#>
#> $postPoints
#> # A tibble: 96 × 7
#> # Groups: id [20]
#> id point Ka Ke V lag prob
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.895 0.0232 75.9 1.76 1.67e- 2
#> 2 1 2 0.432 0.0246 66.2 0.534 9.83e- 1
#> 3 2 1 0.900 0.0431 109. 2.10 7.75e- 8
#> 4 2 2 0.793 0.0439 102. 0.887 1.18e- 3
#> 5 2 3 0.748 0.0396 120. 0.0119 9.52e- 1
#> 6 2 4 0.553 0.0439 118. 0.287 4.73e- 2
#> 7 2 5 0.583 0.0683 73.1 1.34 2.13e-10
#> 8 3 1 0.900 0.0431 109. 2.10 1.00e+ 0
#> 9 3 2 0.583 0.0683 73.1 1.34 7.70e- 9
#> 10 4 1 0.898 0.0564 120. 0.688 1.00e+ 0
#> # ℹ 86 more rows
#>
#> $postMean
#> id Ka Ke V lag
#> 1 1 0.440192 0.0246167 66.3882 0.5544030
#> 2 2 0.738613 0.0398178 119.4690 0.0259495
#> 3 3 0.899944 0.0431027 108.6490 2.0959200
#> 4 4 0.897550 0.0564307 119.8190 0.6883040
#> 5 5 0.105273 0.0675058 113.3430 0.0186591
#> 6 6 0.895237 0.0348831 71.8617 1.9978800
#> 7 7 0.215198 0.0832836 35.2243 1.7966900
#> 8 8 0.895494 0.0348882 71.8466 1.9985200
#> 9 9 0.790042 0.0439417 101.7520 0.8801720
#> 10 10 0.655770 0.0615898 61.6882 0.8013670
#> 11 11 0.583214 0.0683228 73.1084 1.3385500
#> 12 12 0.470094 0.0306855 91.8654 1.0252500
#> 13 13 0.215198 0.0832837 35.2243 1.7957700
#> 14 14 0.555242 0.0439039 117.8380 0.2868650
#> 15 15 0.795724 0.0342102 72.1966 1.0584400
#> 16 16 0.752981 0.0352990 89.6702 0.6879460
#> 17 17 0.891343 0.0734141 63.3168 1.1798900
#> 18 18 0.894633 0.0231919 75.9277 1.7604100
#> 19 19 0.657597 0.0621169 30.9852 1.9241800
#> 20 20 0.215198 0.0832837 35.2243 1.7960100
#>
#> $postSD
#> id Ka Ke V lag
#> 1 1 5.92949e-02 1.85900e-04 1.24471000 1.57358e-01
#> 2 2 4.14333e-02 9.30204e-04 0.71152100 6.54383e-02
#> 3 3 2.78188e-05 2.21253e-06 0.00311777 6.64408e-05
#> 4 4 8.25437e-03 1.15386e-04 0.06746470 6.97672e-03
#> 5 5 5.21762e-02 7.29362e-04 0.42644700 4.41001e-02
#> 6 6 1.63377e-02 1.72654e-04 0.76091100 3.65945e-02
#> 7 7 2.13211e-04 1.51594e-05 0.01146760 1.12733e-02
#> 8 8 1.27877e-02 3.57297e-04 0.60893900 2.66606e-02
#> 9 9 2.55413e-02 2.46894e-04 1.81722000 6.57720e-02
#> 10 10 8.80413e-03 1.13586e-03 2.56904000 6.12762e-03
#> 11 11 9.85709e-03 1.69250e-04 0.36892400 5.74677e-03
#> 12 12 5.57671e-02 7.57640e-04 1.87978000 9.73359e-02
#> 13 13 0.00000e+00 0.00000e+00 0.00000000 0.00000e+00
#> 14 14 2.26544e-02 3.94219e-04 0.99697500 4.43318e-02
#> 15 15 4.62149e-02 4.97149e-04 2.68764000 9.53561e-02
#> 16 16 1.80654e-02 2.97213e-04 0.15839500 2.08028e-02
#> 17 17 3.91285e-02 6.46488e-04 1.24328000 2.01489e-02
#> 18 18 1.70457e-02 5.42301e-05 0.35797400 4.52360e-02
#> 19 19 5.89857e-04 6.74131e-05 0.10113700 1.88146e-04
#> 20 20 4.54947e-05 2.66963e-06 0.00487837 1.15747e-02
#>
#> $postVar
#> id Ka Ke V lag
#> 1 1 3.515885e-03 3.455881e-08 1.549303e+00 2.476154e-02
#> 2 2 1.716718e-03 8.652795e-07 5.062621e-01 4.282171e-03
#> 3 3 7.738856e-10 4.895289e-12 9.720490e-06 4.414380e-09
#> 4 4 6.813462e-05 1.331393e-08 4.551486e-03 4.867462e-05
#> 5 5 2.722356e-03 5.319689e-07 1.818570e-01 1.944819e-03
#> 6 6 2.669204e-04 2.980940e-08 5.789855e-01 1.339157e-03
#> 7 7 4.545893e-08 2.298074e-10 1.315058e-04 1.270873e-04
#> 8 8 1.635253e-04 1.276611e-07 3.708067e-01 7.107876e-04
#> 9 9 6.523580e-04 6.095665e-08 3.302289e+00 4.325956e-03
#> 10 10 7.751271e-05 1.290178e-06 6.599967e+00 3.754773e-05
#> 11 11 9.716222e-05 2.864556e-08 1.361049e-01 3.302537e-05
#> 12 12 3.109969e-03 5.740184e-07 3.533573e+00 9.474277e-03
#> 13 13 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> 14 14 5.132218e-04 1.554086e-07 9.939592e-01 1.965308e-03
#> 15 15 2.135817e-03 2.471571e-07 7.223409e+00 9.092786e-03
#> 16 16 3.263587e-04 8.833557e-08 2.508898e-02 4.327565e-04
#> 17 17 1.531040e-03 4.179467e-07 1.545745e+00 4.059782e-04
#> 18 18 2.905559e-04 2.940904e-09 1.281454e-01 2.046296e-03
#> 19 19 3.479313e-07 4.544526e-09 1.022869e-02 3.539892e-08
#> 20 20 2.069768e-09 7.126924e-12 2.379849e-05 1.339737e-04
#>
#> $postCov
#> , , subj = 1
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 3.515885e-03 -1.102292e-05 0.0738051877 9.330533e-03
#> Ke -1.102292e-05 3.455878e-08 -0.0002313922 -2.925286e-05
#> V 7.380519e-02 -2.313922e-04 1.5493126444 1.958658e-01
#> lag 9.330533e-03 -2.925286e-05 0.1958658182 2.476157e-02
#>
#> , , subj = 2
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.716722e-03 -3.779069e-05 0.0136823127 -2.361088e-03
#> Ke -3.779069e-05 8.652787e-07 -0.0004163413 5.785253e-05
#> V 1.368231e-02 -4.163413e-04 0.5062625240 -3.909039e-02
#> lag -2.361088e-03 5.785253e-05 -0.0390903889 4.282170e-03
#>
#> , , subj = 3
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 7.738863e-10 -6.154988e-11 8.673268e-08 1.848305e-09
#> Ke -6.154988e-11 4.895277e-12 -6.898153e-09 -1.470022e-10
#> V 8.673268e-08 -6.898153e-09 9.720494e-06 2.071473e-07
#> lag 1.848305e-09 -1.470022e-10 2.071473e-07 4.414385e-09
#>
#> , , subj = 4
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 6.813454e-05 -9.524405e-07 5.568784e-04 5.758839e-05
#> Ke -9.524405e-07 1.331399e-08 -7.784503e-06 -8.050177e-07
#> V 5.568784e-04 -7.784503e-06 4.551489e-03 4.706824e-04
#> lag 5.758839e-05 -8.050177e-07 4.706824e-04 4.867461e-05
#>
#> , , subj = 5
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 2.722356e-03 -3.805533e-05 0.0222504089 2.300978e-03
#> Ke -3.805533e-05 5.319685e-07 -0.0003110345 -3.216496e-05
#> V 2.225041e-02 -3.110345e-04 0.1818574305 1.880640e-02
#> lag 2.300978e-03 -3.216496e-05 0.0188063963 1.944823e-03
#>
#> , , subj = 6
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 2.669195e-04 2.602925e-06 -0.0123577534 5.970861e-04
#> Ke 2.602925e-06 2.980950e-08 -0.0001259876 5.927812e-06
#> V -1.235775e-02 -1.259876e-04 0.5789859114 -2.777368e-02
#> lag 5.970861e-04 5.927812e-06 -0.0277736765 1.339157e-03
#>
#> , , subj = 7
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 4.545896e-08 -3.232142e-09 2.445012e-06 1.352866e-08
#> Ke -3.232142e-09 2.298060e-10 -1.738409e-07 -9.618909e-10
#> V 2.445012e-06 -1.738409e-07 1.315051e-04 7.276397e-07
#> lag 1.352866e-08 -9.618909e-10 7.276397e-07 1.270878e-04
#>
#> , , subj = 8
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.635259e-04 -1.934125e-07 -0.0053576255 3.228863e-04
#> Ke -1.934125e-07 1.276614e-07 -0.0001514058 2.671457e-06
#> V -5.357626e-03 -1.514058e-04 0.3708063126 -1.435922e-02
#> lag 3.228863e-04 2.671457e-06 -0.0143592201 7.107889e-04
#>
#> , , subj = 9
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 6.523574e-04 -4.142693e-07 -0.0430318592 1.641200e-03
#> Ke -4.142693e-07 6.095657e-08 -0.0001340919 9.836287e-08
#> V -4.303186e-02 -1.340919e-04 3.3022759234 -1.098566e-01
#> lag 1.641200e-03 9.836287e-08 -0.1098566296 4.325961e-03
#>
#> , , subj = 10
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 7.751278e-05 -9.985336e-06 0.022568488 4.768098e-05
#> Ke -9.985336e-06 1.290186e-06 -0.002917729 -6.014794e-06
#> V 2.256849e-02 -2.917729e-03 6.599960039 1.350484e-02
#> lag 4.768098e-05 -6.014794e-06 0.013504838 3.754774e-05
#>
#> , , subj = 11
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 9.716231e-05 1.506918e-06 -2.761677e-03 -5.281318e-05
#> Ke 1.506918e-06 2.864566e-08 -6.023641e-05 -6.712859e-07
#> V -2.761677e-03 -6.023641e-05 1.361046e-01 1.008036e-03
#> lag -5.281318e-05 -6.712859e-07 1.008036e-03 3.302534e-05
#>
#> , , subj = 12
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 3.109966e-03 4.109677e-05 -0.084613908 1.343227e-03
#> Ke 4.109677e-05 5.740184e-07 -0.000925752 1.405407e-06
#> V -8.461391e-02 -9.257520e-04 3.533566089 -1.357273e-01
#> lag 1.343227e-03 1.405407e-06 -0.135727321 9.474270e-03
#>
#> , , subj = 13
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 0 0 0 0.0000000000
#> Ke 0 0 0 0.0000000000
#> V 0 0 0 0.0000000000
#> lag 0 0 0 0.0001362316
#>
#> , , subj = 14
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 5.132202e-04 -6.792630e-06 -1.137316e-02 9.105388e-05
#> Ke -6.792630e-06 1.554086e-07 -6.773826e-05 9.844952e-06
#> V -1.137316e-02 -6.773826e-05 9.939589e-01 -3.958429e-02
#> lag 9.105388e-05 9.844952e-06 -3.958429e-02 1.965311e-03
#>
#> , , subj = 15
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 2.135819e-03 2.289351e-05 -0.12147786 1.116040e-03
#> Ke 2.289351e-05 2.471571e-07 -0.00132394 8.168564e-06
#> V -1.214779e-01 -1.323940e-03 7.22340714 -1.262943e-02
#> lag 1.116040e-03 8.168564e-06 -0.01262943 9.092790e-03
#>
#> , , subj = 16
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 3.263599e-04 5.282809e-06 -2.681502e-03 -3.757654e-04
#> Ke 5.282809e-06 8.833559e-08 -4.632774e-05 -6.077011e-06
#> V -2.681502e-03 -4.632774e-05 2.508889e-02 3.080597e-03
#> lag -3.757654e-04 -6.077011e-06 3.080597e-03 4.327545e-04
#>
#> , , subj = 17
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.531043e-03 2.529613e-05 -0.0486476844 -0.0007883960
#> Ke 2.529613e-05 4.179465e-07 -0.0008037645 -0.0000130260
#> V -4.864768e-02 -8.037645e-04 1.5457418769 0.0250506637
#> lag -7.883960e-04 -1.302600e-05 0.0250506637 0.0004059771
#>
#> , , subj = 18
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 2.905547e-04 -9.114520e-07 6.099822e-03 7.710707e-04
#> Ke -9.114520e-07 2.940902e-09 -1.921861e-05 -2.417231e-06
#> V 6.099822e-03 -1.921861e-05 1.281454e-01 1.618592e-02
#> lag 7.710707e-04 -2.417231e-06 1.618592e-02 2.046297e-03
#>
#> , , subj = 19
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 3.479309e-07 -3.975948e-08 5.964751e-05 1.095929e-07
#> Ke -3.975948e-08 4.544523e-09 -6.817964e-06 -1.250086e-08
#> V 5.964751e-05 -6.817964e-06 1.022878e-02 1.875083e-05
#> lag 1.095929e-07 -1.250086e-08 1.875083e-05 3.539881e-08
#>
#> , , subj = 20
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 2.069768e-09 -1.214542e-10 2.219400e-07 9.065267e-10
#> Ke -1.214542e-10 7.126941e-12 -1.302346e-08 -5.319506e-11
#> V 2.219400e-07 -1.302346e-08 2.379850e-05 9.720634e-08
#> lag 9.065267e-10 -5.319506e-11 9.720634e-08 1.339727e-04
#>
#>
#> $postCor
#> , , subj = 1
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1 -1 1 1
#> Ke -1 1 -1 -1
#> V 1 -1 1 1
#> lag 1 -1 1 1
#>
#> , , subj = 2
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.9805209 0.4641108 -0.8708237
#> Ke -0.9805209 1.0000000 -0.6290477 0.9504131
#> V 0.4641108 -0.6290477 1.0000000 -0.8395569
#> lag -0.8708237 0.9504131 -0.8395569 1.0000000
#>
#> , , subj = 3
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1 -1 1 1
#> Ke -1 1 -1 -1
#> V 1 -1 1 1
#> lag 1 -1 1 1
#>
#> , , subj = 4
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1 -1 1 1
#> Ke -1 1 -1 -1
#> V 1 -1 1 1
#> lag 1 -1 1 1
#>
#> , , subj = 5
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1 -1 1 1
#> Ke -1 1 -1 -1
#> V 1 -1 1 1
#> lag 1 -1 1 1
#>
#> , , subj = 6
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 0.9227716 -0.9940664 0.9986908
#> Ke 0.9227716 1.0000000 -0.9589949 0.9382127
#> V -0.9940664 -0.9589949 1.0000000 -0.9974327
#> lag 0.9986908 0.9382127 -0.9974327 1.0000000
#>
#> , , subj = 7
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.000000000 -1.000000000 1.000000000 0.005628507
#> Ke -1.000000000 1.000000000 -1.000000000 -0.005628507
#> V 1.000000000 -1.000000000 1.000000000 0.005628507
#> lag 0.005628507 -0.005628507 0.005628507 1.000000000
#>
#> , , subj = 8
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.0423313 -0.6880271 0.9470787
#> Ke -0.0423313 1.0000000 -0.6958878 0.2804453
#> V -0.6880271 -0.6958878 1.0000000 -0.8844779
#> lag 0.9470787 0.2804453 -0.8844779 1.0000000
#>
#> , , subj = 9
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.065694600 -0.9271300 0.976961544
#> Ke -0.0656946 1.000000000 -0.2988724 0.006057307
#> V -0.9271300 -0.298872351 1.0000000 -0.919132806
#> lag 0.9769615 0.006057307 -0.9191328 1.000000000
#>
#> , , subj = 10
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.9985041 0.9978038 0.8838258
#> Ke -0.9985041 1.0000000 -0.9998807 -0.8641770
#> V 0.9978038 -0.9998807 1.0000000 0.8578806
#> lag 0.8838258 -0.8641770 0.8578806 1.0000000
#>
#> , , subj = 11
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 0.9032568 -0.7594297 -0.9323302
#> Ke 0.9032568 1.0000000 -0.9647021 -0.6901675
#> V -0.7594297 -0.9647021 1.0000000 0.4754622
#> lag -0.9323302 -0.6901675 0.4754622 1.0000000
#>
#> , , subj = 12
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 0.97267331 -0.8071557 0.24745643
#> Ke 0.9726733 1.00000000 -0.6500176 0.01905751
#> V -0.8071557 -0.65001765 1.0000000 -0.74180164
#> lag 0.2474564 0.01905751 -0.7418016 1.00000000
#>
#> , , subj = 13
#>
#> par2
#> par1 Ka Ke V lag
#> Ka NaN NaN NaN NaN
#> Ke NaN NaN NaN NaN
#> V NaN NaN NaN NaN
#> lag NaN NaN NaN 1
#>
#> , , subj = 14
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.00000000 -0.7605865 -0.5035529 0.09066318
#> Ke -0.76058654 1.0000000 -0.1723504 0.56332693
#> V -0.50355289 -0.1723504 1.0000000 -0.89561837
#> lag 0.09066318 0.5633269 -0.8956184 1.00000000
#>
#> , , subj = 15
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 0.9964226 -0.97801133 0.25324973
#> Ke 0.9964226 1.0000000 -0.99085598 0.17231003
#> V -0.9780113 -0.9908560 1.00000000 -0.04927926
#> lag 0.2532497 0.1723100 -0.04927926 1.00000000
#>
#> , , subj = 16
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 0.9838947 -0.9371066 -0.9998793
#> Ke 0.9838947 1.0000000 -0.9840848 -0.9828819
#> V -0.9371066 -0.9840848 1.0000000 0.9349176
#> lag -0.9998793 -0.9828819 0.9349176 1.0000000
#>
#> , , subj = 17
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1 1 -1 -1
#> Ke 1 1 -1 -1
#> V -1 -1 1 1
#> lag -1 -1 1 1
#>
#> , , subj = 18
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.9860062 0.9996588 0.9999907
#> Ke -0.9860062 1.0000000 -0.9899891 -0.9853568
#> V 0.9996588 -0.9899891 1.0000000 0.9995435
#> lag 0.9999907 -0.9853568 0.9995435 1.0000000
#>
#> , , subj = 19
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.0000000 -0.9998850 0.9998479 0.9875102
#> Ke -0.9998850 1.0000000 -0.9999970 -0.9856016
#> V 0.9998479 -0.9999970 1.0000000 0.9854039
#> lag 0.9875102 -0.9856016 0.9854039 1.0000000
#>
#> , , subj = 20
#>
#> par2
#> par1 Ka Ke V lag
#> Ka 1.000000000 -1.000000000 1.000000000 0.001721518
#> Ke -1.000000000 1.000000000 -1.000000000 -0.001721518
#> V 1.000000000 -1.000000000 1.000000000 0.001721518
#> lag 0.001721518 -0.001721518 0.001721518 1.000000000
#>
#>
#> $postMed
#> id Ka Ke V lag
#> 1 1 0.4320679 0.02425660 66.45764 0.54033955
#> 2 2 0.7438061 0.03914022 119.52708 0.02101887
#> 3 3 0.8960000 0.04307500 108.75000 2.10000000
#> 4 4 0.8959996 0.05594505 119.54995 0.69999785
#> 5 5 0.1040173 0.06782285 113.25195 0.02008672
#> 6 6 0.8959943 0.03515430 71.85063 1.97997170
#> 7 7 0.2160000 0.08366500 34.95000 1.81206790
#> 8 8 0.8959968 0.03515465 71.85031 1.97998410
#> 9 9 0.7919535 0.04406497 101.55520 0.89976788
#> 10 10 0.6560165 0.06188295 61.95186 0.82008218
#> 11 11 0.5840040 0.06782547 72.74957 1.33998020
#> 12 12 0.4641139 0.03021910 91.63719 1.01977840
#> 13 13 0.2160000 0.08366500 34.95000 1.81054350
#> 14 14 0.5520477 0.04406093 117.75206 0.29990845
#> 15 15 0.7999679 0.03416107 71.85822 1.05983960
#> 16 16 0.7519847 0.03515311 89.85172 0.70007653
#> 17 17 0.8959356 0.07375703 62.85724 1.18032190
#> 18 18 0.8959946 0.02327567 76.34939 1.77997280
#> 19 19 0.6560000 0.06188500 31.35000 1.94000010
#> 20 20 0.2160000 0.08366500 34.95000 1.81096390
#>
#> $shrinkage
#> Ka Ke V lag
#> 1 0.01242742 0.0005819863 0.001606317 0.00685773
#>
#> $gridpts
#> [1] 20011
#>
#> $nsub
#> [1] 20
#>
#> $ab
#> [,1] [,2]
#> [1,] 1e-01 0.9
#> [2,] 1e-03 0.1
#> [3,] 3e+01 120.0
#> [4,] 0e+00 4.0
#>
#> attr(,"class")
#> [1] "PMfinal" "NPAG" "list"
names(final)
#> [1] "popPoints" "popMean" "popSD" "popCV" "popVar"
#> [6] "popCov" "popCor" "popMedian" "popRanFix" "postPoints"
#> [11] "postMean" "postSD" "postVar" "postCov" "postCor"
#> [16] "postMed" "shrinkage" "gridpts" "nsub" "ab"
final2 <- makeFinal(ITex$ITdata)
final2
#> $popMean
#> Ka Ke V lag
#> 1 1.01972 0.045305 85.886 1.46089
#>
#> $popSD
#> Ka Ke V lag
#> 1 0.407229 0.0159415 37.8487 0.509122
#>
#> $popCV
#> Ka Ke V lag
#> 1 39.9355 35.1872 44.0685 34.8501
#>
#> $popVar
#> Ka Ke V lag
#> 1 0.1658355 0.0002541314 1432.524 0.2592052
#>
#> $popCov
#> Ka Ke V lag
#> Ka 0.093286510 -0.0031803374 8.9118735 -4.455791e-02
#> Ke -0.003180337 0.0002212130 -0.1991275 1.865823e-04
#> V 8.911873523 -0.1991274978 1274.5087774 -1.259831e+01
#> lag -0.044557910 0.0001865823 -12.5983075 2.140464e-01
#>
#> $popCor
#> Ka Ke V lag
#> Ka 1.0000000 -0.7000974 0.8173126 -0.3153272
#> Ke -0.7000974 1.0000000 -0.3750200 0.0271151
#> V 0.8173126 -0.3750200 1.0000000 -0.7627581
#> lag -0.3153272 0.0271151 -0.7627581 1.0000000
#>
#> $popMedian
#> Ka Ke V lag
#> 1 1.04484 0.0428514 77.9248 1.53526
#>
#> $postMean
#> id Ka Ke V lag
#> 1 1 1.129990 0.0205478 76.5444 1.626660
#> 2 2 1.357230 0.0324923 147.7150 0.543685
#> 3 3 1.418290 0.0450661 99.9225 2.074560
#> 4 4 1.150940 0.0518714 130.2580 0.766163
#> 5 5 1.591560 0.0438692 167.3070 0.850261
#> 6 6 1.006470 0.0389694 62.8183 1.938970
#> 7 7 0.963543 0.0488749 59.6357 2.030000
#> 8 8 1.032520 0.0391828 63.9842 1.982430
#> 9 9 1.145760 0.0417743 105.9730 1.232490
#> 10 10 0.858861 0.0537188 70.6835 1.310950
#> 11 11 0.849380 0.0624143 80.8237 1.475380
#> 12 12 1.354470 0.0262502 106.2990 1.595150
#> 13 13 0.257586 0.0744322 39.2744 1.610980
#> 14 14 1.059360 0.0418335 123.7810 0.716939
#> 15 15 1.057150 0.0311889 79.3053 1.459240
#> 16 16 1.108320 0.0344111 90.2250 1.379610
#> 17 17 0.755282 0.0700918 67.5731 1.180660
#> 18 18 1.013160 0.0282763 62.9503 1.791790
#> 19 19 0.570312 0.0628438 30.6302 1.901900
#> 20 20 0.714180 0.0579909 52.0175 1.750000
#>
#> $postSD
#> id Ka Ke V lag
#> 1 1 0.299732 0.00565518 18.62770 0.2701600
#> 2 2 0.320341 0.00499317 22.73500 0.2422900
#> 3 3 0.268738 0.00596440 15.02050 0.0572197
#> 4 4 0.295727 0.00629333 17.69500 0.1231420
#> 5 5 0.320317 0.00563546 21.33880 0.0935929
#> 6 6 0.249506 0.00596204 10.74950 0.1009190
#> 7 7 0.271959 0.00579324 8.74851 0.3606920
#> 8 8 0.249261 0.00600871 10.91420 0.0930868
#> 9 9 0.291400 0.00581365 16.73990 0.3039630
#> 10 10 0.278485 0.00656339 10.61970 0.2915090
#> 11 11 0.278675 0.00654308 10.80940 0.2223330
#> 12 12 0.307533 0.00539695 21.19700 0.2763850
#> 13 13 0.100955 0.01292290 7.56560 0.1911950
#> 14 14 0.303632 0.00593398 19.01410 0.1738000
#> 15 15 0.290280 0.00597887 15.97080 0.2809980
#> 16 16 0.291584 0.00586331 16.60240 0.2864610
#> 17 17 0.272600 0.00678617 8.73285 0.3220030
#> 18 18 0.279528 0.00619569 13.94280 0.1848430
#> 19 19 0.226145 0.00762608 4.41272 0.1062260
#> 20 20 0.285015 0.00684034 8.17684 0.3595550
#>
#> $postVar
#> id Ka Ke V lag
#> 1 1 0.08983927 3.198106e-05 346.99121 0.072986426
#> 2 2 0.10261836 2.493175e-05 516.88022 0.058704444
#> 3 3 0.07222011 3.557407e-05 225.61542 0.003274094
#> 4 4 0.08745446 3.960600e-05 313.11302 0.015163952
#> 5 5 0.10260298 3.175841e-05 455.34439 0.008759631
#> 6 6 0.06225324 3.554592e-05 115.55175 0.010184645
#> 7 7 0.07396170 3.356163e-05 76.53643 0.130098719
#> 8 8 0.06213105 3.610460e-05 119.11976 0.008665152
#> 9 9 0.08491396 3.379853e-05 280.22425 0.092393505
#> 10 10 0.07755390 4.307809e-05 112.77803 0.084977497
#> 11 11 0.07765976 4.281190e-05 116.84313 0.049431963
#> 12 12 0.09457655 2.912707e-05 449.31281 0.076388668
#> 13 13 0.01019191 1.670013e-04 57.23830 0.036555528
#> 14 14 0.09219239 3.521212e-05 361.53600 0.030206440
#> 15 15 0.08426248 3.574689e-05 255.06645 0.078959876
#> 16 16 0.08502123 3.437840e-05 275.63969 0.082059905
#> 17 17 0.07431076 4.605210e-05 76.26267 0.103685932
#> 18 18 0.07813590 3.838657e-05 194.40167 0.034166935
#> 19 19 0.05114156 5.815710e-05 19.47210 0.011283963
#> 20 20 0.08123355 4.679025e-05 66.86071 0.129279798
#>
#> $postMed
#> id X1 X2 X3 X4
#> 1 1 1.1725606 0.02129534 74.06495 1.6276689
#> 2 2 1.3883977 0.03317120 144.45040 0.5498802
#> 3 3 1.4942498 0.04520522 99.14373 2.0754900
#> 4 4 1.1989619 0.05220644 128.90381 0.7751335
#> 5 5 1.6458539 0.04427601 165.24575 0.8559370
#> 6 6 1.0833952 0.03885700 62.78955 1.9417860
#> 7 7 1.0427318 0.04858538 59.78889 2.0300000
#> 8 8 1.1121369 0.03903037 64.02096 1.9842501
#> 9 9 1.2007115 0.04207777 104.85960 1.2410648
#> 10 10 0.9016617 0.05380257 70.32123 1.3160047
#> 11 11 0.9272884 0.06222125 80.73287 1.5113966
#> 12 12 1.4084108 0.02686801 103.76674 1.6116705
#> 13 13 0.2606130 0.07413613 39.42300 1.5823084
#> 14 14 1.1016885 0.04225238 122.26484 0.7220736
#> 15 15 1.1059247 0.03167587 77.88155 1.4623435
#> 16 16 1.1589821 0.03484994 88.82377 1.3861270
#> 17 17 0.7881036 0.06996770 67.45016 1.1811075
#> 18 18 1.0745367 0.02857761 62.14914 1.7942337
#> 19 19 0.6375351 0.06168130 31.13954 1.9110003
#> 20 20 0.7961764 0.05718801 52.64695 1.7500000
#>
#> $shrinkage
#> Ka Ke V lag
#> 1 0.4656046 0.1730608 0.1547893 0.2155101
#>
#> $nsub
#> [1] 20
#>
#> $ab
#> [,1] [,2]
#> [1,] 1e-08 3.0558655
#> [2,] 1e-08 0.1250127
#> [3,] 1e-08 275.1293673
#> [4,] 1e-08 4.0065024
#>
#> attr(,"class")
#> [1] "PMfinal" "IT2B" "list"
names(final2)
#> [1] "popMean" "popSD" "popCV" "popVar" "popCov" "popCor"
#> [7] "popMedian" "postMean" "postSD" "postVar" "postMed" "shrinkage"
#> [13] "nsub" "ab"