![[Stable]](figures/lifecycle-stable.svg)
Parses the cycle information from an NPAG or an IT2B object.
Value
The output of makeCycle is a list of class PMcycle,
which has 8 objects from NPAG or 6 objects from IT2B :
- names
Vector of names of the random parameters
#'
- names
Vector of names of the random parameters
- cycnum
Vector cycle numbers, which may start at numbers greater than 1 if a non-uniform prior was specified for the run (NPAG only)
- ll
Matrix of cycle number and -2*Log-likelihood at each cycle
- gamlam
A matrix of cycle number and gamma or lambda at each cycle
- mean
A matrix of cycle number and the mean of each random parameter at each cycle, normalized to initial mean
- sd
A matrix of cycle number and the standard deviation of each random parameter at each cycle, normalized to initial standard deviation
- median
A matrix of cycle number and the median of each random parameter at each cycle, normalized to initial median
- aic
A matrix of cycle number and Akaike Information Criterion at each cycle
- bic
A matrix of cycle number and Bayesian (Schwartz) Information Criterion at each cycle
A plot method exists in plot for PMcycle 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 cycle information.
Examples
library(PmetricsData)
cycle <- makeCycle(NPex$NPdata)
cycle
#> $names
#> [1] "Ka" "Ke" "V" "lag"
#>
#> $cycnum
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100
#>
#> $ll
#> [1] 507.3346 481.2885 460.5295 456.6656 456.4747 456.4747 454.6416 453.0673
#> [9] 452.7149 452.6453 452.6453 448.5969 448.4804 448.3917 448.3405 448.3009
#> [17] 448.2713 448.2713 446.7067 445.8884 445.5819 445.5588 445.5471 445.5426
#> [25] 445.5426 444.4183 443.9666 443.4177 443.0373 442.8305 442.7360 442.7092
#> [33] 442.6847 442.6652 442.5090 442.2605 442.1442 442.1415 442.1399 442.1396
#> [41] 442.1369 442.1322 442.1288 442.1267 442.1259 442.1214 442.1164 442.1128
#> [49] 442.1106 442.1077 442.1014 442.0966 442.0932 442.0902 442.0829 442.0771
#> [57] 442.0728 442.0681 442.0600 442.0535 442.0486 442.0447 442.0353 442.0278
#> [65] 442.0218 442.0172 442.0141 442.0124 442.0120 442.0120 441.6645 441.4615
#> [73] 441.3766 441.3248 441.2395 441.1680 441.1068 441.0444 440.9916 440.8840
#> [81] 440.7468 440.6802 440.5175 440.4220 440.4022 440.3955 440.3622 440.3499
#> [89] 440.3384 440.3066 440.2895 440.2692 440.2470 440.2440 440.2243 440.2052
#> [97] 440.2048 440.1825 440.1657 440.1657
#>
#> $gamlam
#> # A tibble: 100 × 3
#> cycle value outeq
#> <int> <dbl> <chr>
#> 1 1 4.55 1
#> 2 2 3.79 1
#> 3 3 2.71 1
#> 4 4 2.71 1
#> 5 5 2.71 1
#> 6 6 2.71 1
#> 7 7 2.71 1
#> 8 8 2.58 1
#> 9 9 2.58 1
#> 10 10 2.58 1
#> # ℹ 90 more rows
#>
#> $mean
#> # A tibble: 100 × 5
#> cycle Ka Ke V lag
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 1 1
#> 2 2 1.01 1.00 0.993 0.997
#> 3 3 1.03 1.01 0.989 0.997
#> 4 4 1.02 1.01 0.995 0.984
#> 5 5 0.994 1.01 0.995 0.983
#> 6 6 0.994 1.01 0.995 0.983
#> 7 7 1.01 1.01 1.01 0.954
#> 8 8 1.00 1.01 1.00 0.949
#> 9 9 1.01 1.01 1.00 0.950
#> 10 10 1.02 1.01 1.00 0.950
#> # ℹ 90 more rows
#>
#> $sd
#> # A tibble: 100 × 5
#> cycle Ka Ke V lag
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 1 1
#> 2 2 1.03 1.00 0.982 1.00
#> 3 3 1.07 1.01 0.971 1.01
#> 4 4 1.04 1.01 0.973 1.18
#> 5 5 1.03 1.01 0.973 1.18
#> 6 6 1.03 1.01 0.973 1.18
#> 7 7 1.03 1.01 1.01 1.10
#> 8 8 1.03 1.01 1.01 1.14
#> 9 9 1.05 1.01 1.00 1.14
#> 10 10 1.06 1.01 1.00 1.14
#> # ℹ 90 more rows
#>
#> $median
#> # A tibble: 100 × 5
#> cycle Ka Ke V lag
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 1 1
#> 2 2 1.04 1.00 1.00 1.04
#> 3 3 1.06 1.00 1.00 1.05
#> 4 4 1.04 1.00 1.00 0.802
#> 5 5 1.04 1.00 1.00 0.801
#> 6 6 1.04 1.00 1.00 0.801
#> 7 7 1.06 1.00 1.00 0.801
#> 8 8 1.06 1.00 1.00 0.822
#> 9 9 1.06 1.00 1.00 0.825
#> 10 10 1.07 1.00 1.00 0.825
#> # ℹ 90 more rows
#>
#> $aic
#> 1 2 3 4 5 6 7 8
#> 517.7858 491.7396 470.9806 467.1167 466.9258 466.9258 465.0927 463.5185
#> 9 10 11 12 13 14 15 16
#> 463.1660 463.0964 463.0964 459.0480 458.9315 458.8429 458.7916 458.7521
#> 17 18 19 20 21 22 23 24
#> 458.7225 458.7225 457.1578 456.3396 456.0331 456.0099 455.9982 455.9937
#> 25 26 27 28 29 30 31 32
#> 455.9937 454.8695 454.4178 453.8688 453.4884 453.2816 453.1872 453.1603
#> 33 34 35 36 37 38 39 40
#> 453.1359 453.1163 452.9602 452.7117 452.5954 452.5926 452.5911 452.5907
#> 41 42 43 44 45 46 47 48
#> 452.5880 452.5833 452.5799 452.5778 452.5770 452.5725 452.5675 452.5639
#> 49 50 51 52 53 54 55 56
#> 452.5617 452.5588 452.5525 452.5477 452.5443 452.5413 452.5340 452.5282
#> 57 58 59 60 61 62 63 64
#> 452.5239 452.5192 452.5112 452.5047 452.4998 452.4958 452.4864 452.4789
#> 65 66 67 68 69 70 71 72
#> 452.4729 452.4684 452.4652 452.4635 452.4631 452.4631 452.1157 451.9126
#> 73 74 75 76 77 78 79 80
#> 451.8277 451.7759 451.6906 451.6191 451.5580 451.4955 451.4427 451.3352
#> 81 82 83 84 85 86 87 88
#> 451.1979 451.1314 450.9686 450.8731 450.8533 450.8467 450.8133 450.8010
#> 89 90 91 92 93 94 95 96
#> 450.7896 450.7577 450.7407 450.7203 450.6981 450.6951 450.6755 450.6563
#> 97 98 99 100
#> 450.6560 450.6336 450.6168 450.6168
#>
#> $bic
#> 1 2 3 4 5 6 7 8
#> 532.0070 505.9609 485.2018 481.3380 481.1471 481.1471 479.3139 477.7397
#> 9 10 11 12 13 14 15 16
#> 477.3873 477.3177 477.3177 473.2692 473.1527 473.0641 473.0129 472.9733
#> 17 18 19 20 21 22 23 24
#> 472.9437 472.9437 471.3791 470.5608 470.2543 470.2312 470.2194 470.2150
#> 25 26 27 28 29 30 31 32
#> 470.2150 469.0907 468.6390 468.0901 467.7097 467.5029 467.4084 467.3816
#> 33 34 35 36 37 38 39 40
#> 467.3571 467.3375 467.1814 466.9329 466.8166 466.8138 466.8123 466.8119
#> 41 42 43 44 45 46 47 48
#> 466.8092 466.8046 466.8012 466.7990 466.7982 466.7938 466.7888 466.7852
#> 49 50 51 52 53 54 55 56
#> 466.7829 466.7801 466.7738 466.7690 466.7656 466.7625 466.7552 466.7494
#> 57 58 59 60 61 62 63 64
#> 466.7452 466.7405 466.7324 466.7259 466.7210 466.7171 466.7077 466.7002
#> 65 66 67 68 69 70 71 72
#> 466.6941 466.6896 466.6865 466.6847 466.6844 466.6844 466.3369 466.1339
#> 73 74 75 76 77 78 79 80
#> 466.0489 465.9972 465.9119 465.8404 465.7792 465.7168 465.6639 465.5564
#> 81 82 83 84 85 86 87 88
#> 465.4192 465.3526 465.1899 465.0944 465.0746 465.0679 465.0345 465.0223
#> 89 90 91 92 93 94 95 96
#> 465.0108 464.9789 464.9619 464.9416 464.9193 464.9163 464.8967 464.8776
#> 97 98 99 100
#> 464.8772 464.8549 464.8381 464.8381
#>
#> attr(,"class")
#> [1] "PMcycle" "list"
names(cycle)
#> [1] "names" "cycnum" "ll" "gamlam" "mean" "sd" "median" "aic"
#> [9] "bic"