Auxiliary function for forecasting
Usage
# S3 method for fitted_dlm
forecast(
object,
t = 1,
plot = ifelse(requireNamespace("plotly", quietly = TRUE), "plotly",
ifelse(requireNamespace("ggplot2", quietly = TRUE), "ggplot2", "base")),
pred.cred = 0.95,
...
)Arguments
- object
fitted_dlm object: The fitted model to be use for predictions.
- t
numeric: Time window for prediction.
- plot
boolean or character: A flag indicating if a plot should be produced. Should be one of FALSE, TRUE, 'base', 'ggplot2' or 'plotly'.
- pred.cred
numeric: The credibility level for the C.I..
- ...
Extra variables necessary for prediction (covariates, etc.).
Value
A list containing:
data data.frame: A data frame contain the mean, variance and credibility intervals for the outcomes, including both the observed data and the predictions for future observations.
forecast data.frame: Same as data, but restricted to predictions for future observations.
outcomes list: A named list containing predictions for each outcome. Each element of this list is a list containing predictions (mean, variance and credibility intervals), the distribution of the linear predictor for the parameter of the observational model and the parameters of the predictive distribution (if available).
theta.mean matrix: A matrix with the values for the latent states at each time. Dimensions are n x t, where n is the number of latent states
theta.cov array: A 3D-array with the covariance of the latent states at each time. Dimensions are n x n x t, where n is the number of latent predictors.
lambda.mean matrix: A matrix with the values for the linear predictors at each time. Dimensions are k x t, where k is the number of linear predictors
lambda.cov array: A 3D-array with the covariance of the linear predictors at each time. Dimensions are k x k x t, where k is the number of linear predictors.
plot (if so chosen): A plotly or ggplot object.
A list containing:
data data.frame: A table with the model evaluated at each observed time, plus the forecasted period.
forecast data.frame: A table with the model evaluated at the forecasted period.
outcomes list: A list containing the parameters of the predictive distribution for each outcome at the forecasted period.
theta.mean matrix: The mean of the latent states at each forecasted time. Dimensions are n x t.forecast, where t.forecast is the size of the forecast windows and n is the number of latent states.
theta.cov array: A 3D-array containing the covariance matrix of the latent states at each forecasted time. Dimensions are n x n x t.forecast, where t.forecast is the size of the forecast windows and n is the number of latent states.
lambda.mean matrix: The mean of the linear predictor at each forecasted time. Dimensions are k x t.forecast, where t.forecast is the size of the forecast windows and k is the number of linear predictors.
lambda.cov array: A 3D-array containing the covariance matrix for the linear predictor at each forecasted time. Dimensions are k x k x t.forecast, where t.forecast is the size of the forecast windows and k is the number of linear predictors.
Details
If an a covariate is necessary for forecasting, it should be passed as a named argument. Its name must follow this structure: <block name>.Covariate<.index>. If there is only one covariate in the associated block the index is omitted. If an a pulse is necessary for forecasting, it should be passed as a named argument. Its name must follow this structure: <block name>.Pulse<.index>. If there is only one pulse in the associated block the index is omitted. The user may pass the observed values at the prediction windows (optional). See example. As an special case, if the model has an Multinomial outcome, the user may pass the N parameter instead of the observations. If an offset is necessary for forecasting, it should be passed with the same syntax as the observed data. See example.
See also
Other auxiliary functions for fitted_dlm objects:
coef.fitted_dlm(),
eval_dlm_norm_const(),
fit_model(),
simulate.fitted_dlm(),
smoothing(),
update.fitted_dlm()
Examples
structure <-
polynomial_block(p = 1, order = 2, D = 0.95) +
harmonic_block(p = 1, period = 12, D = 0.975) +
noise_block(p = 1, R1 = 0.1) +
regression_block(
p = chickenPox$date >= as.Date("2013-09-1"),
# Vaccine was introduced in September of 2013
name = "Vaccine"
)
outcome <- Multinom(p = c("p.1", "p.2"), data = chickenPox[, c(2, 3, 5)])
fitted.data <- fit_model(structure * 2,
chickenPox = outcome
)
forecast(fitted.data, 24,
chickenPox = list(Total = rep(175, 24)), # Optional
Vaccine.1.Covariate = rep(TRUE, 24),
Vaccine.2.Covariate = rep(TRUE, 24)
)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> $data
#> Time Serie Observation Prediction Variance C.I.lower
#> 1 1 chickenPox.< 5 year 218 213.56974 1944.8860 127
#> 2 2 chickenPox.< 5 year 215 214.00177 1360.3010 142
#> 3 3 chickenPox.< 5 year 256 250.66942 1675.6397 170
#> 4 4 chickenPox.< 5 year 271 265.06469 1562.4174 186
#> 5 5 chickenPox.< 5 year 348 336.74105 2074.3296 244
#> 6 6 chickenPox.< 5 year 333 322.77968 1687.9260 239
#> 7 7 chickenPox.< 5 year 397 387.58815 2287.1740 290
#> 8 8 chickenPox.< 5 year 459 478.80415 3251.8539 362
#> 9 9 chickenPox.< 5 year 811 775.68707 6277.8685 611
#> 10 10 chickenPox.< 5 year 932 896.40928 8250.1825 708
#> 11 11 chickenPox.< 5 year 730 698.85927 5911.6826 541
#> 12 12 chickenPox.< 5 year 460 455.27873 3354.9501 338
#> 13 13 chickenPox.< 5 year 214 227.74120 1116.6561 162
#> 14 14 chickenPox.< 5 year 133 148.73901 562.4871 102
#> 15 15 chickenPox.< 5 year 162 156.09889 612.3608 108
#> 16 16 chickenPox.< 5 year 227 197.27490 890.1128 139
#> 17 17 chickenPox.< 5 year 231 213.21307 956.3178 152
#> 18 18 chickenPox.< 5 year 179 196.88730 742.3365 143
#> 19 19 chickenPox.< 5 year 256 262.68672 1087.5723 196
#> 20 20 chickenPox.< 5 year 343 333.38235 1469.9184 256
#> 21 21 chickenPox.< 5 year 403 428.46452 2198.4787 333
#> 22 22 chickenPox.< 5 year 544 515.88506 3055.9950 403
#> 23 23 chickenPox.< 5 year 518 483.16453 2858.2936 374
#> 24 24 chickenPox.< 5 year 446 423.31318 2538.7060 322
#> 25 25 chickenPox.< 5 year 248 253.52498 1125.8304 187
#> 26 26 chickenPox.< 5 year 126 134.89325 400.4484 96
#> 27 27 chickenPox.< 5 year 113 143.58286 486.8892 101
#> 28 28 chickenPox.< 5 year 152 140.82415 458.8652 99
#> 29 29 chickenPox.< 5 year 157 154.45380 492.0809 111
#> 30 30 chickenPox.< 5 year 181 177.76916 537.7032 132
#> 31 31 chickenPox.< 5 year 222 226.94000 697.2585 174
#> 32 32 chickenPox.< 5 year 292 284.92774 905.0167 224
#> 33 33 chickenPox.< 5 year 521 489.54092 2337.6367 391
#> 34 34 chickenPox.< 5 year 587 574.53647 3233.9224 458
#> 35 35 chickenPox.< 5 year 495 456.20134 2279.2700 359
#> 36 36 chickenPox.< 5 year 363 322.96270 1376.3427 248
#> 37 37 chickenPox.< 5 year 157 185.38548 580.0217 137
#> 38 38 chickenPox.< 5 year 102 118.02545 292.8830 84
#> 39 39 chickenPox.< 5 year 132 136.74766 413.3607 97
#> 40 40 chickenPox.< 5 year 155 144.03607 454.3267 102
#> 41 41 chickenPox.< 5 year 217 211.17818 864.9089 153
#> 42 42 chickenPox.< 5 year 364 326.42901 1749.6136 244
#> 43 43 chickenPox.< 5 year 333 322.01557 1505.9702 244
#> 44 44 chickenPox.< 5 year 308 318.64367 1374.7130 244
#> 45 45 chickenPox.< 5 year 535 534.19956 3076.1298 421
#> 46 46 chickenPox.< 5 year 705 682.39558 4940.4660 539
#> 47 47 chickenPox.< 5 year 623 569.46819 3723.4364 445
#> 48 48 chickenPox.< 5 year 542 464.81368 2866.8987 357
#> 49 49 chickenPox.< 5 year 295 277.10907 1267.9996 206
#> 50 50 chickenPox.< 5 year 108 124.79874 335.7050 89
#> 51 51 chickenPox.< 5 year 85 108.42661 289.9193 75
#> 52 52 chickenPox.< 5 year 122 124.89530 383.6504 87
#> 53 53 chickenPox.< 5 year 145 159.13240 572.7219 112
#> 54 54 chickenPox.< 5 year 208 187.37599 695.5368 135
#> 55 55 chickenPox.< 5 year 191 201.93811 723.6880 149
#> 56 56 chickenPox.< 5 year 191 195.93937 634.1869 146
#> 57 57 chickenPox.< 5 year 289 291.05078 1297.0990 219
#> 58 58 chickenPox.< 5 year 374 358.52661 1984.8137 269
#> 59 59 chickenPox.< 5 year 305 285.26500 1375.5849 211
#> 60 60 chickenPox.< 5 year 297 262.80329 1306.5271 191
#> 61 61 chickenPox.< 5 year 156 156.70840 559.5527 111
#> 62 62 chickenPox.< 5 year 64 77.28622 171.4541 52
#> 63 63 chickenPox.< 5 year 97 98.03901 277.0873 66
#> 64 64 chickenPox.< 5 year 119 97.65991 275.6222 66
#> 65 65 chickenPox.< 5 year 96 100.83344 289.2801 68
#> 66 66 chickenPox.< 5 year 108 113.75408 343.7215 78
#> 67 67 chickenPox.< 5 year 127 141.24812 484.9784 98
#> 68 68 chickenPox.< 5 year 143 147.09456 500.8693 103
#> 69 69 chickenPox.< 5 year 191 185.07468 764.9394 131
#> 70 70 chickenPox.< 5 year 195 192.06278 849.4044 135
#> 71 71 chickenPox.< 5 year 153 147.21681 545.8692 102
#> 72 72 chickenPox.< 5 year 134 128.46826 453.9837 87
#> 73 73 chickenPox.< 5 year 79 92.58644 268.2073 61
#> 74 74 chickenPox.< 5 year 76 72.12331 178.7961 47
#> 75 75 chickenPox.< 5 year 88 81.13450 224.7404 53
#> 76 76 chickenPox.< 5 year 76 79.53448 215.1652 52
#> 77 77 chickenPox.< 5 year 84 91.31704 265.2800 60
#> 78 78 chickenPox.< 5 year 94 89.40094 241.5017 60
#> 79 79 chickenPox.< 5 year 126 123.78420 421.6119 84
#> 80 80 chickenPox.< 5 year 135 134.14956 487.2676 91
#> 81 81 chickenPox.< 5 year 152 149.08468 607.8418 101
#> 82 82 chickenPox.< 5 year 193 192.93190 1031.5099 131
#> 83 83 chickenPox.< 5 year 151 146.79368 642.2902 98
#> 84 84 chickenPox.< 5 year 146 133.67929 558.6623 89
#> 85 85 chickenPox.< 5 year 105 108.34111 390.2422 71
#> 86 86 chickenPox.< 5 year 72 76.92751 213.2169 49
#> 87 87 chickenPox.< 5 year 90 93.75019 303.3861 61
#> 88 88 chickenPox.< 5 year 77 73.56937 192.1087 47
#> 89 89 chickenPox.< 5 year 90 86.41401 248.4576 56
#> 90 90 chickenPox.< 5 year 87 94.25762 282.1017 62
#> 91 91 chickenPox.< 5 year 110 105.92999 342.7935 71
#> 92 92 chickenPox.< 5 year 134 129.83282 506.9293 87
#> 93 93 chickenPox.< 5 year 132 134.28783 562.2911 89
#> 94 94 chickenPox.< 5 year 151 150.04932 724.3239 99
#> 95 95 chickenPox.< 5 year 138 128.53728 561.1646 84
#> 96 96 chickenPox.< 5 year 104 104.28359 389.3101 67
#> 97 97 chickenPox.< 5 year 67 91.44475 308.2993 58
#> 98 98 chickenPox.< 5 year 58 64.34194 161.5043 40
#> 99 99 chickenPox.< 5 year 94 85.10786 254.1365 55
#> 100 100 chickenPox.< 5 year 79 72.60289 184.3695 47
#> 101 101 chickenPox.< 5 year 93 93.45190 278.0868 62
#> 102 102 chickenPox.< 5 year 84 76.15672 187.1512 50
#> 103 103 chickenPox.< 5 year 96 99.99475 304.1102 67
#> 104 104 chickenPox.< 5 year 82 83.92861 226.5444 55
#> 105 105 chickenPox.< 5 year 80 88.93602 260.4904 58
#> 106 106 chickenPox.< 5 year 92 91.21889 283.4215 59
#> 107 107 chickenPox.< 5 year 77 80.61587 232.7325 52
#> 108 108 chickenPox.< 5 year 79 81.15405 236.0541 52
#> 109 109 chickenPox.< 5 year 73 72.56088 189.9907 47
#> 110 110 chickenPox.< 5 year 54 63.60043 146.3446 41
#> 111 111 chickenPox.< 5 year 133 100.45350 312.1120 67
#> 112 112 chickenPox.< 5 year 162 102.69799 319.2682 69
#> 113 113 chickenPox.< 5 year 102 85.18424 234.5429 56
#> 114 114 chickenPox.< 5 year 70 74.47020 191.4051 48
#> 115 115 chickenPox.< 5 year 79 80.13631 229.4112 52
#> 116 116 chickenPox.< 5 year 59 74.59278 218.5768 47
#> 117 117 chickenPox.< 5 year 49 68.03241 200.5202 42
#> 118 118 chickenPox.< 5 year 63 64.14745 192.6557 38
#> 119 119 chickenPox.< 5 year 63 66.60609 219.2651 39
#> 120 120 chickenPox.< 5 year 48 51.25208 146.9337 29
#> 121 1 chickenPox.5 to 9 years 64 65.10525 1944.8860 16
#> 122 2 chickenPox.5 to 9 years 43 51.61887 1360.3010 14
#> 123 3 chickenPox.5 to 9 years 60 60.18322 1675.6397 17
#> 124 4 chickenPox.5 to 9 years 47 53.97332 1562.4174 14
#> 125 5 chickenPox.5 to 9 years 66 68.13572 2074.3296 20
#> C.I.upper type
#> 1 298 Fit
#> 2 285 Fit
#> 3 329 Fit
#> 4 340 Fit
#> 5 422 Fit
#> 6 399 Fit
#> 7 476 Fit
#> 8 584 Fit
#> 9 920 Fit
#> 10 1062 Fit
#> 11 840 Fit
#> 12 564 Fit
#> 13 292 Fit
#> 14 195 Fit
#> 15 204 Fit
#> 16 255 Fit
#> 17 273 Fit
#> 18 249 Fit
#> 19 325 Fit
#> 20 405 Fit
#> 21 516 Fit
#> 22 618 Fit
#> 23 583 Fit
#> 24 518 Fit
#> 25 318 Fit
#> 26 174 Fit
#> 27 187 Fit
#> 28 183 Fit
#> 29 197 Fit
#> 30 222 Fit
#> 31 277 Fit
#> 32 341 Fit
#> 33 579 Fit
#> 34 680 Fit
#> 35 545 Fit
#> 36 393 Fit
#> 37 231 Fit
#> 38 151 Fit
#> 39 176 Fit
#> 40 186 Fit
#> 41 268 Fit
#> 42 407 Fit
#> 43 396 Fit
#> 44 388 Fit
#> 45 637 Fit
#> 46 813 Fit
#> 47 683 Fit
#> 48 566 Fit
#> 49 345 Fit
#> 50 160 Fit
#> 51 142 Fit
#> 52 163 Fit
#> 53 206 Fit
#> 54 238 Fit
#> 55 254 Fit
#> 56 244 Fit
#> 57 359 Fit
#> 58 443 Fit
#> 59 356 Fit
#> 60 332 Fit
#> 61 203 Fit
#> 62 103 Fit
#> 63 131 Fit
#> 64 131 Fit
#> 65 134 Fit
#> 66 150 Fit
#> 67 184 Fit
#> 68 191 Fit
#> 69 239 Fit
#> 70 249 Fit
#> 71 193 Fit
#> 72 170 Fit
#> 73 125 Fit
#> 74 99 Fit
#> 75 111 Fit
#> 76 109 Fit
#> 77 124 Fit
#> 78 120 Fit
#> 79 164 Fit
#> 80 178 Fit
#> 81 198 Fit
#> 82 256 Fit
#> 83 197 Fit
#> 84 181 Fit
#> 85 148 Fit
#> 86 106 Fit
#> 87 129 Fit
#> 88 101 Fit
#> 89 118 Fit
#> 90 128 Fit
#> 91 143 Fit
#> 92 175 Fit
#> 93 182 Fit
#> 94 204 Fit
#> 95 176 Fit
#> 96 144 Fit
#> 97 127 Fit
#> 98 90 Fit
#> 99 117 Fit
#> 100 100 Fit
#> 101 127 Fit
#> 102 104 Fit
#> 103 135 Fit
#> 104 114 Fit
#> 105 121 Fit
#> 106 125 Fit
#> 107 111 Fit
#> 108 112 Fit
#> 109 100 Fit
#> 110 88 Fit
#> 111 136 Fit
#> 112 139 Fit
#> 113 116 Fit
#> 114 102 Fit
#> 115 111 Fit
#> 116 105 Fit
#> 117 97 Fit
#> 118 93 Fit
#> 119 97 Fit
#> 120 76 Fit
#> 121 140 Fit
#> 122 108 Fit
#> 123 124 Fit
#> 124 114 Fit
#> 125 139 Fit
#> [ reached 'max' / getOption("max.print") -- omitted 307 rows ]
#>
#> $forecast
#> Time Serie Observation Variance Prediction C.I.lower
#> 1 121 chickenPox.< 5 year NA 149.4347 49.63073 27
#> 2 122 chickenPox.< 5 year NA 149.4347 48.42213 26
#> 3 123 chickenPox.< 5 year NA 149.4347 47.78840 25
#> 4 124 chickenPox.< 5 year NA 149.4347 47.79864 25
#> 5 125 chickenPox.< 5 year NA 149.4347 48.32058 24
#> 6 126 chickenPox.< 5 year NA 149.4347 49.00718 24
#> 7 127 chickenPox.< 5 year NA 149.4347 49.40287 24
#> 8 128 chickenPox.< 5 year NA 149.4347 49.14310 23
#> 9 129 chickenPox.< 5 year NA 149.4347 48.13517 21
#> 10 130 chickenPox.< 5 year NA 149.4347 46.56745 19
#> 11 131 chickenPox.< 5 year NA 149.4347 44.74058 17
#> 12 132 chickenPox.< 5 year NA 149.4347 42.90972 16
#> 13 133 chickenPox.< 5 year NA 149.4347 41.27574 15
#> 14 134 chickenPox.< 5 year NA 149.4347 40.03981 13
#> 15 135 chickenPox.< 5 year NA 149.4347 39.38220 13
#> 16 136 chickenPox.< 5 year NA 149.4347 39.37087 12
#> 17 137 chickenPox.< 5 year NA 149.4347 39.88988 12
#> 18 138 chickenPox.< 5 year NA 149.4347 40.63895 12
#> 19 139 chickenPox.< 5 year NA 149.4347 41.21720 11
#> 20 140 chickenPox.< 5 year NA 149.4347 41.27810 10
#> 21 141 chickenPox.< 5 year NA 149.4347 40.67993 9
#> 22 142 chickenPox.< 5 year NA 149.4347 39.51596 8
#> 23 143 chickenPox.< 5 year NA 149.4347 38.00802 7
#> 24 144 chickenPox.< 5 year NA 149.4347 36.39376 6
#> 25 121 chickenPox.5 to 9 years NA 149.4347 21.84474 7
#> 26 122 chickenPox.5 to 9 years NA 149.4347 18.81722 5
#> 27 123 chickenPox.5 to 9 years NA 149.4347 16.63545 4
#> 28 124 chickenPox.5 to 9 years NA 149.4347 15.57295 3
#> 29 125 chickenPox.5 to 9 years NA 149.4347 15.65854 3
#> 30 126 chickenPox.5 to 9 years NA 149.4347 16.81260 3
#> 31 127 chickenPox.5 to 9 years NA 149.4347 18.83476 4
#> 32 128 chickenPox.5 to 9 years NA 149.4347 21.28412 5
#> 33 129 chickenPox.5 to 9 years NA 149.4347 23.42408 5
#> 34 130 chickenPox.5 to 9 years NA 149.4347 24.43135 5
#> 35 131 chickenPox.5 to 9 years NA 149.4347 23.83661 5
#> 36 132 chickenPox.5 to 9 years NA 149.4347 21.84320 4
#> 37 133 chickenPox.5 to 9 years NA 149.4347 19.20289 3
#> 38 134 chickenPox.5 to 9 years NA 149.4347 16.75421 2
#> 39 135 chickenPox.5 to 9 years NA 149.4347 15.04688 1
#> 40 136 chickenPox.5 to 9 years NA 149.4347 14.29210 1
#> 41 137 chickenPox.5 to 9 years NA 149.4347 14.50365 1
#> 42 138 chickenPox.5 to 9 years NA 149.4347 15.60631 1
#> 43 139 chickenPox.5 to 9 years NA 149.4347 17.42073 1
#> 44 140 chickenPox.5 to 9 years NA 149.4347 19.56488 1
#> 45 141 chickenPox.5 to 9 years NA 149.4347 21.41461 1
#> 46 142 chickenPox.5 to 9 years NA 149.4347 22.28607 1
#> 47 143 chickenPox.5 to 9 years NA 149.4347 21.80527 1
#> 48 144 chickenPox.5 to 9 years NA 149.4347 20.16313 1
#> 49 121 chickenPox.15 to 49 years NA 149.4347 96.52452 70
#> 50 122 chickenPox.15 to 49 years NA 149.4347 100.76065 74
#> 51 123 chickenPox.15 to 49 years NA 149.4347 103.57615 76
#> 52 124 chickenPox.15 to 49 years NA 149.4347 104.62841 77
#> 53 125 chickenPox.15 to 49 years NA 149.4347 104.02089 75
#> 54 126 chickenPox.15 to 49 years NA 149.4347 102.18022 72
#> 55 127 chickenPox.15 to 49 years NA 149.4347 99.76237 69
#> 56 128 chickenPox.15 to 49 years NA 149.4347 97.57278 65
#> 57 129 chickenPox.15 to 49 years NA 149.4347 96.44075 63
#> 58 130 chickenPox.15 to 49 years NA 149.4347 97.00120 62
#> 59 131 chickenPox.15 to 49 years NA 149.4347 99.42281 64
#> 60 132 chickenPox.15 to 49 years NA 149.4347 103.24708 67
#> 61 133 chickenPox.15 to 49 years NA 149.4347 107.52137 71
#> 62 134 chickenPox.15 to 49 years NA 149.4347 111.20599 75
#> 63 135 chickenPox.15 to 49 years NA 149.4347 113.57093 76
#> 64 136 chickenPox.15 to 49 years NA 149.4347 114.33703 76
#> 65 137 chickenPox.15 to 49 years NA 149.4347 113.60648 74
#> 66 138 chickenPox.15 to 49 years NA 149.4347 111.75474 71
#> 67 139 chickenPox.15 to 49 years NA 149.4347 109.36207 66
#> 68 140 chickenPox.15 to 49 years NA 149.4347 107.15702 62
#> 69 141 chickenPox.15 to 49 years NA 149.4347 105.90546 60
#> 70 142 chickenPox.15 to 49 years NA 149.4347 106.19797 59
#> 71 143 chickenPox.15 to 49 years NA 149.4347 108.18671 60
#> 72 144 chickenPox.15 to 49 years NA 149.4347 111.44311 63
#> C.I.upper
#> 1 75
#> 2 74
#> 3 74
#> 4 75
#> 5 76
#> 6 78
#> 7 80
#> 8 80
#> 9 81
#> 10 80
#> 11 79
#> 12 77
#> 13 76
#> 14 75
#> 15 75
#> 16 75
#> 17 77
#> 18 80
#> 19 82
#> 20 84
#> 21 85
#> 22 85
#> 23 84
#> 24 82
#> 25 42
#> 26 38
#> 27 36
#> 28 35
#> 29 36
#> 30 38
#> 31 42
#> 32 46
#> 33 50
#> 34 53
#> 35 53
#> 36 50
#> 37 47
#> 38 44
#> 39 42
#> 40 41
#> 41 43
#> 42 46
#> 43 50
#> 44 55
#> 45 59
#> 46 62
#> 47 62
#> 48 60
#> 49 122
#> 50 126
#> 51 129
#> 52 131
#> 53 131
#> 54 130
#> 55 129
#> 56 128
#> 57 128
#> 58 129
#> 59 132
#> 60 136
#> 61 140
#> 62 143
#> 63 145
#> 64 146
#> 65 147
#> 66 146
#> 67 146
#> 68 145
#> 69 146
#> 70 147
#> 71 149
#> 72 151
#>
#> $outcomes
#> $outcomes$chickenPox
#> $outcomes$chickenPox$conj.param
#> alpha_1 alpha_2 alpha_3
#> 121 14.776264 6.503706 28.737675
#> 122 13.732912 5.336718 28.576545
#> 123 12.827580 4.465362 27.802380
#> 124 12.015628 3.914730 26.301504
#> 125 11.245151 3.644050 24.207711
#> 126 10.459954 3.588434 21.809055
#> 127 9.622953 3.668735 19.432245
#> 128 8.745467 3.787705 17.363973
#> 129 7.886815 3.837972 15.801552
#> 130 7.117000 3.733894 14.824896
#> 131 6.478673 3.451667 14.396949
#> 132 5.975437 3.041797 14.377776
#> 133 5.580470 2.596226 14.536864
#> 134 5.254112 2.198524 14.592696
#> 135 4.962072 1.895874 14.309693
#> 136 4.683021 1.699992 13.599970
#> 137 4.403397 1.601041 12.540888
#> 138 4.111537 1.578927 11.306488
#> 139 3.801517 1.606737 10.086608
#> 140 3.482312 1.650537 9.040004
#> 141 3.178261 1.673091 8.274233
#> 142 2.916131 1.644629 7.837015
#> 143 2.710720 1.555145 7.715842
#> 144 2.559856 1.418230 7.838660
#>
#> $outcomes$chickenPox$ft
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.6819068 -0.7520426 -0.7949168 -0.8064766 -0.7910596 -0.7602321
#> [2,] -1.5471819 -1.7569829 -1.9268112 -2.0188749 -2.0162187 -1.9272676
#> [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] -0.7296896 -0.7150511 -0.7276742 -0.7716119 -0.8425262 -0.928851 -1.014891
#> [2,] -1.7835693 -1.6313410 -1.5190853 -1.4845943 -1.5448231 -1.691347 -1.892618
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] -1.085027 -1.127901 -1.139461 -1.124044 -1.093216 -1.062674 -1.048035
#> [2,] -2.102419 -2.272247 -2.364311 -2.361654 -2.272703 -2.129005 -1.976777
#> [,21] [,22] [,23] [,24]
#> [1,] -1.060658 -1.104596 -1.175510 -1.261835
#> [2,] -1.864521 -1.830030 -1.890259 -2.036783
#>
#> $outcomes$chickenPox$Qt
#> , , 1
#>
#> [,1] [,2]
#> [1,] 0.126574059 0.002780609
#> [2,] 0.002780609 0.131677338
#>
#> , , 2
#>
#> [,1] [,2]
#> [1,] 0.140113999 0.003076489
#> [2,] 0.003076489 0.147655162
#>
#> , , 3
#>
#> [,1] [,2]
#> [1,] 0.154158661 0.003318991
#> [2,] 0.003318991 0.164206083
#>
#> , , 4
#>
#> [,1] [,2]
#> [1,] 0.1677064 0.0034983
#> [2,] 0.0034983 0.1800504
#>
#> , , 5
#>
#> [,1] [,2]
#> [1,] 0.180224168 0.003625945
#> [2,] 0.003625945 0.194479798
#>
#> , , 6
#>
#> [,1] [,2]
#> [1,] 0.19177331 0.00373013
#> [2,] 0.00373013 0.20756617
#>
#> , , 7
#>
#> [,1] [,2]
#> [1,] 0.202936715 0.003848195
#> [2,] 0.003848195 0.220082065
#>
#> , , 8
#>
#> [,1] [,2]
#> [1,] 0.214673490 0.004018534
#> [2,] 0.004018534 0.233273283
#>
#> , , 9
#>
#> [,1] [,2]
#> [1,] 0.228174208 0.004272023
#> [2,] 0.004272023 0.248616919
#>
#> , , 10
#>
#> [,1] [,2]
#> [1,] 0.244673580 0.004622896
#> [2,] 0.004622896 0.267573558
#>
#> , , 11
#>
#> [,1] [,2]
#> [1,] 0.265150698 0.005061447
#> [2,] 0.005061447 0.291265577
#>
#> , , 12
#>
#> [,1] [,2]
#> [1,] 0.289959108 0.005553042
#> [2,] 0.005553042 0.320090109
#>
#> , , 13
#>
#> [,1] [,2]
#> [1,] 0.318573350 0.006046547
#> [2,] 0.006046547 0.353433294
#>
#> , , 14
#>
#> [,1] [,2]
#> [1,] 0.349654318 0.006490469
#> [2,] 0.006490469 0.389713270
#>
#> , , 15
#>
#> [,1] [,2]
#> [1,] 0.381472833 0.006850211
#> [2,] 0.006850211 0.426840044
#>
#> , , 16
#>
#> [,1] [,2]
#> [1,] 0.412508942 0.007119014
#> [2,] 0.007119014 0.462927711
#>
#> , , 17
#>
#> [,1] [,2]
#> [1,] 0.4419450 0.0073189
#> [2,] 0.0073189 0.4969327
#>
#> , , 18
#>
#> [,1] [,2]
#> [1,] 0.469868017 0.007493187
#> [2,] 0.007493187 0.528952268
#>
#> , , 19
#>
#> [,1] [,2]
#> [1,] 0.497189435 0.007694902
#> [2,] 0.007694902 0.560138432
#>
#> , , 20
#>
#> [,1] [,2]
#> [1,] 0.525412243 0.007974499
#> [2,] 0.007974499 0.592368608
#>
#> , , 21
#>
#> [,1] [,2]
#> [1,] 0.556340237 0.008368047
#> [2,] 0.008368047 0.627834567
#>
#> , , 22
#>
#> [,1] [,2]
#> [1,] 0.591726449 0.008886725
#> [2,] 0.008886725 0.668603095
#>
#> , , 23
#>
#> [,1] [,2]
#> [1,] 0.632834496 0.009510335
#> [2,] 0.009510335 0.716131861
#>
#> , , 24
#>
#> [,1] [,2]
#> [1,] 0.67999242 0.01018913
#> [2,] 0.01018913 0.77079255
#>
#>
#> $outcomes$chickenPox$data
#> [,1] [,2] [,3]
#> [1,] 48 26 94
#> [2,] 48 26 94
#> [3,] 48 26 94
#> [4,] 48 26 94
#> [5,] 48 26 94
#> [6,] 48 26 94
#> [7,] 48 26 94
#> [8,] 48 26 94
#> [9,] 48 26 94
#> [10,] 48 26 94
#> [11,] 48 26 94
#> [12,] 48 26 94
#> [13,] 48 26 94
#> [14,] 48 26 94
#> [15,] 48 26 94
#> [16,] 48 26 94
#> [17,] 48 26 94
#> [18,] 48 26 94
#> [19,] 48 26 94
#> [20,] 48 26 94
#> [21,] 48 26 94
#> [22,] 48 26 94
#> [23,] 48 26 94
#> [24,] 48 26 94
#>
#> $outcomes$chickenPox$offset
#> [,1] [,2] [,3]
#> [1,] 1 1 1
#> [2,] 1 1 1
#> [3,] 1 1 1
#> [4,] 1 1 1
#> [5,] 1 1 1
#> [6,] 1 1 1
#> [7,] 1 1 1
#> [8,] 1 1 1
#> [9,] 1 1 1
#> [10,] 1 1 1
#> [11,] 1 1 1
#> [12,] 1 1 1
#> [13,] 1 1 1
#> [14,] 1 1 1
#> [15,] 1 1 1
#> [16,] 1 1 1
#> [17,] 1 1 1
#> [18,] 1 1 1
#> [19,] 1 1 1
#> [20,] 1 1 1
#> [21,] 1 1 1
#> [22,] 1 1 1
#> [23,] 1 1 1
#> [24,] 1 1 1
#>
#> $outcomes$chickenPox$pred
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 49.63073 48.42213 47.78840 47.79864 48.32058 49.00718 49.40287
#> [2,] 21.84474 18.81722 16.63545 15.57295 15.65854 16.81260 18.83476
#> [3,] 96.52452 100.76065 103.57615 104.62841 104.02089 102.18022 99.76237
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 49.14310 48.13517 46.56745 44.74058 42.90972 41.27574 40.03981
#> [2,] 21.28412 23.42408 24.43135 23.83661 21.84320 19.20289 16.75421
#> [3,] 97.57278 96.44075 97.00120 99.42281 103.24708 107.52137 111.20599
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21]
#> [1,] 39.38220 39.37087 39.88988 40.63895 41.21720 41.27810 40.67993
#> [2,] 15.04688 14.29210 14.50365 15.60631 17.42073 19.56488 21.41461
#> [3,] 113.57093 114.33703 113.60648 111.75474 109.36207 107.15702 105.90546
#> [,22] [,23] [,24]
#> [1,] 39.51596 38.00802 36.39376
#> [2,] 22.28607 21.80527 20.16313
#> [3,] 106.19797 108.18671 111.44311
#>
#> $outcomes$chickenPox$var.pred
#> , , 1
#>
#> [,1] [,2] [,3]
#> [1,] 149.4347 -27.57780 -121.85695
#> [2,] -27.5778 81.21259 -53.63479
#> [3,] -121.8569 -53.63479 175.49174
#>
#> , , 2
#>
#> [,1] [,2] [,3]
#> [1,] 152.7842 -24.04270 -128.74151
#> [2,] -24.0427 74.07267 -50.02997
#> [3,] -128.7415 -50.02997 178.77149
#>
#> , , 3
#>
#> [,1] [,2] [,3]
#> [1,] 158.07990 -21.87584 -136.2041
#> [2,] -21.87584 69.28934 -47.4135
#> [3,] -136.20406 -47.41350 183.6176
#>
#> , , 4
#>
#> [,1] [,2] [,3]
#> [1,] 166.30688 -21.54625 -144.76063
#> [2,] -21.54625 68.70973 -47.16348
#> [3,] -144.76063 -47.16348 191.92411
#>
#> , , 5
#>
#> [,1] [,2] [,3]
#> [1,] 177.78905 -23.26144 -154.52761
#> [2,] -23.26144 73.33692 -50.07548
#> [3,] -154.52761 -50.07548 204.60309
#>
#> , , 6
#>
#> [,1] [,2] [,3]
#> [1,] 191.98733 -27.12606 -164.86127
#> [2,] -27.12606 83.68403 -56.55797
#> [3,] -164.86127 -56.55797 221.41924
#>
#> , , 7
#>
#> [,1] [,2] [,3]
#> [1,] 207.57641 -32.96583 -174.6106
#> [2,] -32.96583 99.53583 -66.5700
#> [3,] -174.61058 -66.57000 241.1806
#>
#> , , 8
#>
#> [,1] [,2] [,3]
#> [1,] 222.68904 -39.87771 -182.8113
#> [2,] -39.87771 119.05421 -79.1765
#> [3,] -182.81134 -79.17650 261.9878
#>
#> , , 9
#>
#> [,1] [,2] [,3]
#> [1,] 235.39879 -46.00183 -189.39696
#> [2,] -46.00183 138.16834 -92.16651
#> [3,] -189.39696 -92.16651 281.56347
#>
#> , , 10
#>
#> [,1] [,2] [,3]
#> [1,] 244.38037 -49.16756 -195.2128
#> [2,] -49.16756 151.58487 -102.4173
#> [3,] -195.21281 -102.41730 297.6301
#>
#> , , 11
#>
#> [,1] [,2] [,3]
#> [1,] 249.26693 -48.20466 -201.0623
#> [2,] -48.20466 155.32537 -107.1207
#> [3,] -201.06227 -107.12071 308.1830
#>
#> , , 12
#>
#> [,1] [,2] [,3]
#> [1,] 250.66840 -43.77158 -206.8968
#> [2,] -43.77158 149.09243 -105.3208
#> [3,] -206.89682 -105.32085 312.2177
#>
#> , , 13
#>
#> [,1] [,2] [,3]
#> [1,] 250.39760 -37.94347 -212.45413
#> [2,] -37.94347 136.78442 -98.84096
#> [3,] -212.45413 -98.84096 311.29509
#>
#> , , 14
#>
#> [,1] [,2] [,3]
#> [1,] 251.49642 -32.92917 -218.56725
#> [2,] -32.92917 124.38618 -91.45701
#> [3,] -218.56725 -91.45701 310.02426
#>
#> , , 15
#>
#> [,1] [,2] [,3]
#> [1,] 257.28776 -30.09986 -227.18791
#> [2,] -30.09986 116.90225 -86.80239
#> [3,] -227.18791 -86.80239 313.99030
#>
#> , , 16
#>
#> [,1] [,2] [,3]
#> [1,] 270.05759 -30.00634 -240.05125
#> [2,] -30.00634 117.14780 -87.14146
#> [3,] -240.05125 -87.14146 327.19272
#>
#> , , 17
#>
#> [,1] [,2] [,3]
#> [1,] 290.32090 -32.86791 -257.4530
#> [2,] -32.86791 126.47582 -93.6079
#> [3,] -257.45298 -93.60790 351.0609
#>
#> , , 18
#>
#> [,1] [,2] [,3]
#> [1,] 316.69079 -38.80601 -277.8848
#> [2,] -38.80601 145.52028 -106.7143
#> [3,] -277.88477 -106.71427 384.5990
#>
#> , , 19
#>
#> [,1] [,2] [,3]
#> [1,] 346.02285 -47.54564 -298.4772
#> [2,] -47.54564 173.69904 -126.1534
#> [3,] -298.47721 -126.15340 424.6306
#>
#> , , 20
#>
#> [,1] [,2] [,3]
#> [1,] 373.83374 -57.71704 -316.1167
#> [2,] -57.71704 207.54918 -149.8321
#> [3,] -316.11670 -149.83214 465.9488
#>
#> , , 21
#>
#> [,1] [,2] [,3]
#> [1,] 395.31303 -66.48971 -328.8233
#> [2,] -66.48971 239.58794 -173.0982
#> [3,] -328.82332 -173.09823 501.9216
#>
#> , , 22
#>
#> [,1] [,2] [,3]
#> [1,] 406.9218 -70.5822 -336.3396
#> [2,] -70.5822 260.2698 -189.6876
#> [3,] -336.3396 -189.6876 526.0272
#>
#> , , 23
#>
#> [,1] [,2] [,3]
#> [1,] 407.73603 -68.39494 -339.3411
#> [2,] -68.39494 263.07551 -194.6806
#> [3,] -339.34108 -194.68057 534.0217
#>
#> , , 24
#>
#> [,1] [,2] [,3]
#> [1,] 399.9876 -61.2813 -338.7063
#> [2,] -61.2813 248.9337 -187.6524
#> [3,] -338.7063 -187.6524 526.3587
#>
#>
#> $outcomes$chickenPox$icl.pred
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 27 26 25 25 24 24 24 23 21 19 17 16 15 13
#> [2,] 7 5 4 3 3 3 4 5 5 5 5 4 3 2
#> [3,] 70 74 76 77 75 72 69 65 63 62 64 67 71 75
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 13 12 12 12 11 10 9 8 7 6
#> [2,] 1 1 1 1 1 1 1 1 1 1
#> [3,] 76 76 74 71 66 62 60 59 60 63
#>
#> $outcomes$chickenPox$icu.pred
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 75 74 74 75 76 78 80 80 81 80 79 77 76 75
#> [2,] 42 38 36 35 36 38 42 46 50 53 53 50 47 44
#> [3,] 122 126 129 131 131 130 129 128 128 129 132 136 140 143
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 75 75 77 80 82 84 85 85 84 82
#> [2,] 42 41 43 46 50 55 59 62 62 60
#> [3,] 145 146 147 146 146 145 146 147 149 151
#>
#>
#> $outcomes$show
#> [1] FALSE
#>
#>
#> $theta.mean
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -1.00169702 -1.02944570 -1.05719437 -1.08494305 -1.11269173 -1.14044040
#> [2,] -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868
#> [3,] -0.05935463 -0.10174180 -0.11686733 -0.10067836 -0.05751270 0.00106344
#> [4,] -0.10067836 -0.05751270 0.00106344 0.05935463 0.10174180 0.11686733
#> [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [6,] 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486
#> [7,] -2.03920255 -2.06798887 -2.09677519 -2.12556151 -2.15434783 -2.18313415
#> [8,] -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632
#> [9,] 0.03183478 -0.14917990 -0.29022195 -0.35349926 -0.32205673 -0.20431936
#> [10,] -0.35349926 -0.32205673 -0.20431936 -0.03183478 0.14917990 0.29022195
#> [11,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [12,] 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] -1.16818908 -1.19593775 -1.22368643 -1.25143510 -1.27918378 -1.30693245
#> [2,] -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868
#> [3,] 0.05935463 0.10174180 0.11686733 0.10067836 0.05751270 -0.00106344
#> [4,] 0.10067836 0.05751270 -0.00106344 -0.05935463 -0.10174180 -0.11686733
#> [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [6,] 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486
#> [7,] -2.21192047 -2.24070679 -2.26949311 -2.29827943 -2.32706575 -2.35585207
#> [8,] -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632
#> [9,] -0.03183478 0.14917990 0.29022195 0.35349926 0.32205673 0.20431936
#> [10,] 0.35349926 0.32205673 0.20431936 0.03183478 -0.14917990 -0.29022195
#> [11,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [12,] 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] -1.33468113 -1.36242981 -1.39017848 -1.41792716 -1.44567583 -1.47342451
#> [2,] -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868
#> [3,] -0.05935463 -0.10174180 -0.11686733 -0.10067836 -0.05751270 0.00106344
#> [4,] -0.10067836 -0.05751270 0.00106344 0.05935463 0.10174180 0.11686733
#> [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [6,] 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486
#> [7,] -2.38463839 -2.41342471 -2.44221102 -2.47099734 -2.49978366 -2.52856998
#> [8,] -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632
#> [9,] 0.03183478 -0.14917990 -0.29022195 -0.35349926 -0.32205673 -0.20431936
#> [10,] -0.35349926 -0.32205673 -0.20431936 -0.03183478 0.14917990 0.29022195
#> [11,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [12,] 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] -1.50117318 -1.52892186 -1.55667053 -1.58441921 -1.61216789 -1.63991656
#> [2,] -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868 -0.02774868
#> [3,] 0.05935463 0.10174180 0.11686733 0.10067836 0.05751270 -0.00106344
#> [4,] 0.10067836 0.05751270 -0.00106344 -0.05935463 -0.10174180 -0.11686733
#> [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [6,] 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486 0.37914486
#> [7,] -2.55735630 -2.58614262 -2.61492894 -2.64371526 -2.67250158 -2.70128790
#> [8,] -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632 -0.02878632
#> [9,] -0.03183478 0.14917990 0.29022195 0.35349926 0.32205673 0.20431936
#> [10,] 0.35349926 0.32205673 0.20431936 0.03183478 -0.14917990 -0.29022195
#> [11,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> [12,] 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590 0.46018590
#>
#> $theta.cov
#> , , 1
#>
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 9.872440e-02 2.030001e-03 2.664470e-04 2.782113e-03 0.00000000
#> [2,] 2.030001e-03 4.887590e-04 -4.027821e-05 1.799244e-04 0.00000000
#> [3,] 2.664470e-04 -4.027821e-05 7.211060e-03 1.560346e-04 0.00000000
#> [4,] 2.782113e-03 1.799244e-04 1.560346e-04 7.336572e-03 0.00000000
#> [5,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.08356629
#> [6,] -6.226665e-02 -4.927366e-09 -7.251207e-04 6.870868e-04 0.00000000
#> [7,] 5.546934e-03 9.903291e-05 1.724105e-04 -1.141960e-05 0.00000000
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#> [5,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.08356629
#> [6,] -6.226668e-02 -4.927366e-09 7.251207e-04 -6.870868e-04 0.00000000
#> [7,] 6.926576e-03 1.311953e-04 -1.963976e-04 -4.056966e-06 0.00000000
#> [8,] 1.309076e-04 5.360397e-06 -3.997851e-06 -2.579428e-06 0.00000000
#> [9,] -1.975763e-04 -3.965807e-06 4.029683e-04 2.452050e-06 0.00000000
#> [10,] -1.324797e-05 -2.786504e-06 2.279165e-06 3.970212e-04 0.00000000
#> [11,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.00000000
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] -6.226668e-02 6.926576e-03 1.309076e-04 -1.975763e-04 -1.324797e-05
#> [2,] -4.927366e-09 1.311953e-04 5.360397e-06 -3.965807e-06 -2.786504e-06
#> [3,] 7.251207e-04 -1.963976e-04 -3.997851e-06 4.029683e-04 2.279165e-06
#> [4,] -6.870868e-04 -4.056966e-06 -2.579428e-06 2.452050e-06 3.970212e-04
#> [5,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [6,] 6.252295e-02 -3.329221e-03 1.037567e-06 1.052795e-04 -1.074222e-04
#> [7,] -3.329221e-03 1.965035e-01 6.193700e-03 3.363109e-06 -4.761856e-03
#> [8,] 1.037567e-06 6.193700e-03 7.347898e-04 4.818086e-05 -2.093564e-04
#> [9,] 1.052795e-04 3.363109e-06 4.818086e-05 9.959445e-03 1.464377e-04
#> [10,] -1.074222e-04 -4.761856e-03 -2.093564e-04 1.464377e-04 1.019652e-02
#> [11,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [,11] [,12]
#> [1,] 0.00000000 -3.329811e-03
#> [2,] 0.00000000 1.057412e-06
#> [3,] 0.00000000 1.024899e-04
#> [4,] 0.00000000 -1.055686e-04
#> [5,] 0.00000000 0.000000e+00
#> [6,] 0.00000000 3.363887e-03
#> [7,] 0.00000000 -6.929859e-02
#> [8,] 0.00000000 3.556783e-07
#> [9,] 0.00000000 8.642072e-04
#> [10,] 0.00000000 -8.397154e-04
#> [11,] 0.08084326 0.000000e+00
#>
#> [ reached getOption("max.print") -- omitted 1 row(s) and 17 matrix slice(s) ]
#>
#> $lambda.mean
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.6819068 -0.7520426 -0.7949168 -0.8064766 -0.7910596 -0.7602321
#> [2,] -1.5471819 -1.7569829 -1.9268112 -2.0188749 -2.0162187 -1.9272676
#> [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] -0.7296896 -0.7150511 -0.7276742 -0.7716119 -0.8425262 -0.928851 -1.014891
#> [2,] -1.7835693 -1.6313410 -1.5190853 -1.4845943 -1.5448231 -1.691347 -1.892618
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] -1.085027 -1.127901 -1.139461 -1.124044 -1.093216 -1.062674 -1.048035
#> [2,] -2.102419 -2.272247 -2.364311 -2.361654 -2.272703 -2.129005 -1.976777
#> [,21] [,22] [,23] [,24]
#> [1,] -1.060658 -1.104596 -1.175510 -1.261835
#> [2,] -1.864521 -1.830030 -1.890259 -2.036783
#>
#> $lambda.cov
#> , , 1
#>
#> [,1] [,2]
#> [1,] 0.126574059 0.002780609
#> [2,] 0.002780609 0.131677338
#>
#> , , 2
#>
#> [,1] [,2]
#> [1,] 0.140113999 0.003076489
#> [2,] 0.003076489 0.147655162
#>
#> , , 3
#>
#> [,1] [,2]
#> [1,] 0.154158661 0.003318991
#> [2,] 0.003318991 0.164206083
#>
#> , , 4
#>
#> [,1] [,2]
#> [1,] 0.1677064 0.0034983
#> [2,] 0.0034983 0.1800504
#>
#> , , 5
#>
#> [,1] [,2]
#> [1,] 0.180224168 0.003625945
#> [2,] 0.003625945 0.194479798
#>
#> , , 6
#>
#> [,1] [,2]
#> [1,] 0.19177331 0.00373013
#> [2,] 0.00373013 0.20756617
#>
#> , , 7
#>
#> [,1] [,2]
#> [1,] 0.202936715 0.003848195
#> [2,] 0.003848195 0.220082065
#>
#> , , 8
#>
#> [,1] [,2]
#> [1,] 0.214673490 0.004018534
#> [2,] 0.004018534 0.233273283
#>
#> , , 9
#>
#> [,1] [,2]
#> [1,] 0.228174208 0.004272023
#> [2,] 0.004272023 0.248616919
#>
#> , , 10
#>
#> [,1] [,2]
#> [1,] 0.244673580 0.004622896
#> [2,] 0.004622896 0.267573558
#>
#> , , 11
#>
#> [,1] [,2]
#> [1,] 0.265150698 0.005061447
#> [2,] 0.005061447 0.291265577
#>
#> , , 12
#>
#> [,1] [,2]
#> [1,] 0.289959108 0.005553042
#> [2,] 0.005553042 0.320090109
#>
#> , , 13
#>
#> [,1] [,2]
#> [1,] 0.318573350 0.006046547
#> [2,] 0.006046547 0.353433294
#>
#> , , 14
#>
#> [,1] [,2]
#> [1,] 0.349654318 0.006490469
#> [2,] 0.006490469 0.389713270
#>
#> , , 15
#>
#> [,1] [,2]
#> [1,] 0.381472833 0.006850211
#> [2,] 0.006850211 0.426840044
#>
#> , , 16
#>
#> [,1] [,2]
#> [1,] 0.412508942 0.007119014
#> [2,] 0.007119014 0.462927711
#>
#> , , 17
#>
#> [,1] [,2]
#> [1,] 0.4419450 0.0073189
#> [2,] 0.0073189 0.4969327
#>
#> , , 18
#>
#> [,1] [,2]
#> [1,] 0.469868017 0.007493187
#> [2,] 0.007493187 0.528952268
#>
#> , , 19
#>
#> [,1] [,2]
#> [1,] 0.497189435 0.007694902
#> [2,] 0.007694902 0.560138432
#>
#> , , 20
#>
#> [,1] [,2]
#> [1,] 0.525412243 0.007974499
#> [2,] 0.007974499 0.592368608
#>
#> , , 21
#>
#> [,1] [,2]
#> [1,] 0.556340237 0.008368047
#> [2,] 0.008368047 0.627834567
#>
#> , , 22
#>
#> [,1] [,2]
#> [1,] 0.591726449 0.008886725
#> [2,] 0.008886725 0.668603095
#>
#> , , 23
#>
#> [,1] [,2]
#> [1,] 0.632834496 0.009510335
#> [2,] 0.009510335 0.716131861
#>
#> , , 24
#>
#> [,1] [,2]
#> [1,] 0.67999242 0.01018913
#> [2,] 0.01018913 0.77079255
#>
#>
#> $plot
#>