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Model Predictions for var_estimate Objects

Usage

# S3 method for class 'var_estimate'
predict(object, summary = TRUE, cred = 0.95, iter = NULL, progress = TRUE, ...)

Arguments

object

object of class var_estimate

summary

summarize the posterior samples (defaults to TRUE).

cred

credible interval used for summarizing

iter

number of posterior samples (defaults to all in the object).

progress

Logical. Should a progress bar be included (defaults to TRUE) ?

...

Currently ignored

Value

The predicted values for each regression model.

Examples

# \donttest{
# data
Y <- subset(ifit, id == 1)[,-1]

# fit model with alias (var_estimate also works)
fit <- var_estimate(Y, progress = FALSE)

# fitted values
pred <- predict(fit, progress = FALSE)

# predicted values (1st outcome)
pred[,,1]
#>          Post.mean   Post.sd       Cred.lb     Cred.ub
#>  [1,]  0.562116637 0.2530684  7.047629e-02  1.06043810
#>  [2,]  0.275213394 0.1829880 -8.213391e-02  0.62982899
#>  [3,] -0.078973235 0.3342252 -7.240503e-01  0.58031397
#>  [4,] -0.142048665 0.3935211 -9.180291e-01  0.63073174
#>  [5,]  0.143452317 0.3454895 -5.186182e-01  0.81591537
#>  [6,]  0.373436112 0.1901275  4.155063e-05  0.74554829
#>  [7,]  0.490776882 0.2105534  8.225338e-02  0.90584507
#>  [8,]  0.477444888 0.2477667 -5.492402e-03  0.95849706
#>  [9,]  0.358277691 0.2172837 -6.272708e-02  0.78555251
#> [10,]  0.427936477 0.1744513  8.661729e-02  0.76726888
#> [11,] -0.674634948 0.4729400 -1.601325e+00  0.25698657
#> [12,] -0.564238131 0.4362605 -1.414978e+00  0.29084354
#> [13,]  0.064403245 0.2536513 -4.499693e-01  0.56658634
#> [14,] -0.384257658 0.3857338 -1.144618e+00  0.38015704
#> [15,]  0.088025890 0.1473245 -2.037425e-01  0.37639015
#> [16,]  0.231742073 0.1989877 -1.617990e-01  0.62382415
#> [17,]  0.309860457 0.2421396 -1.721425e-01  0.77754822
#> [18,]  0.202100856 0.2880493 -3.536688e-01  0.77693557
#> [19,]  0.169258377 0.2484374 -3.131845e-01  0.65779008
#> [20,]  0.163323994 0.2178996 -2.716978e-01  0.57704704
#> [21,] -0.326699373 0.3023613 -9.065613e-01  0.25797574
#> [22,]  0.218361908 0.1688934 -1.110106e-01  0.55359163
#> [23,]  0.242063165 0.1449472 -4.010986e-02  0.52572812
#> [24,]  0.093509260 0.3520775 -5.750133e-01  0.78441305
#> [25,]  0.537272598 0.3116809 -8.955718e-02  1.14422577
#> [26,]  0.201712990 0.2239085 -2.487261e-01  0.62858614
#> [27,]  0.212531307 0.1940822 -1.731132e-01  0.58717516
#> [28,]  0.123700196 0.1510170 -1.790853e-01  0.40742234
#> [29,]  0.073644743 0.2312380 -3.798830e-01  0.50782438
#> [30,] -0.232489078 0.2006420 -6.268097e-01  0.16225760
#> [31,]  0.213313886 0.4150521 -5.875407e-01  1.04383330
#> [32,]  0.035622632 0.1951117 -3.501065e-01  0.41772990
#> [33,] -0.213257984 0.1906426 -5.844457e-01  0.16479344
#> [34,] -0.012209867 0.1864097 -3.767937e-01  0.35814147
#> [35,] -0.859939735 0.4338367 -1.706415e+00 -0.01312883
#> [36,] -0.692267562 0.3817743 -1.426711e+00  0.04219090
#> [37,] -0.682558494 0.4395044 -1.523251e+00  0.18022662
#> [38,] -0.676119912 0.4677507 -1.575312e+00  0.24633977
#> [39,] -0.277524547 0.3451404 -9.344363e-01  0.41958231
#> [40,]  0.184394172 0.1323283 -8.600888e-02  0.43795045
#> [41,] -0.184504919 0.1998146 -5.718976e-01  0.20724847
#> [42,] -0.148870351 0.3347575 -8.131257e-01  0.50104219
#> [43,]  0.014785671 0.2477706 -4.736071e-01  0.48920119
#> [44,]  0.012859619 0.2050689 -3.894685e-01  0.41306760
#> [45,]  0.076565853 0.2689260 -4.523174e-01  0.59686589
#> [46,]  0.273007178 0.1216092  3.457424e-02  0.51474194
#> [47,]  0.275132492 0.2003072 -1.180581e-01  0.67456131
#> [48,] -0.306363784 0.3914251 -1.057938e+00  0.46081877
#> [49,]  0.020521789 0.1237684 -2.231005e-01  0.26096913
#> [50,]  0.067156233 0.2220628 -3.619923e-01  0.50123085
#> [51,]  0.044227166 0.1421637 -2.340321e-01  0.32499866
#> [52,]  0.163504313 0.1568781 -1.493948e-01  0.47576460
#> [53,]  0.095206191 0.1910677 -2.808211e-01  0.46795561
#> [54,]  0.165362850 0.1963778 -2.233029e-01  0.55046273
#> [55,]  0.109075512 0.1494754 -1.871606e-01  0.39659964
#> [56,] -0.012615788 0.1939933 -3.895035e-01  0.36525274
#> [57,]  0.521664466 0.2080154  1.071628e-01  0.92680716
#> [58,]  0.107910441 0.2218408 -3.322177e-01  0.53126818
#> [59,]  0.282004263 0.3779949 -4.719417e-01  1.03025125
#> [60,]  0.024918244 0.2651423 -4.880465e-01  0.54521052
#> [61,] -0.055425698 0.2799560 -5.904878e-01  0.49164044
#> [62,] -0.193032978 0.2446824 -6.871462e-01  0.27313507
#> [63,] -0.155117884 0.2257220 -6.029590e-01  0.29148924
#> [64,]  0.239519161 0.3479886 -4.327973e-01  0.94600359
#> [65,]  0.100606385 0.4008387 -6.864504e-01  0.90032811
#> [66,]  0.272291806 0.2203475 -1.667987e-01  0.69533531
#> [67,] -0.364739417 0.3047781 -9.578492e-01  0.22310333
#> [68,]  0.209301820 0.1679051 -1.227002e-01  0.53704797
#> [69,]  0.152907859 0.2434675 -3.229189e-01  0.62717137
#> [70,] -0.401775385 0.4208413 -1.225738e+00  0.42503991
#> [71,]  0.062696250 0.2438335 -3.950021e-01  0.55479591
#> [72,] -0.119110443 0.1927604 -4.956992e-01  0.25656125
#> [73,] -0.119267612 0.2038521 -5.163000e-01  0.28635172
#> [74,] -0.098703495 0.2033451 -4.910126e-01  0.29974858
#> [75,] -0.158171025 0.1683948 -4.930956e-01  0.16895700
#> [76,] -0.062216206 0.1500910 -3.569366e-01  0.23719837
#> [77,] -0.181047296 0.1342543 -4.423782e-01  0.08814742
#> [78,] -0.059011468 0.2819407 -6.112845e-01  0.49723296
#> [79,] -0.490652864 0.3845842 -1.236765e+00  0.25021028
#> [80,]  0.098953806 0.2096484 -3.129931e-01  0.50992196
#> [81,]  0.005505305 0.1009212 -1.896074e-01  0.20596422
#> [82,] -0.286561083 0.2089615 -6.945650e-01  0.13088511
#> [83,] -0.436187709 0.3801805 -1.170028e+00  0.32348652
#> [84,]  0.433089949 0.2550436 -5.966714e-02  0.92124838
#> [85,]  0.204432921 0.2003199 -1.757346e-01  0.59252022
#> [86,]  0.142392426 0.3924371 -6.046808e-01  0.93382160
#> [87,] -0.198069141 0.1700985 -5.286237e-01  0.12498131
#> [88,] -0.058882983 0.2202599 -4.897746e-01  0.37594688
#> [89,] -0.290144118 0.2144236 -7.093497e-01  0.13578498
#> [90,]  0.124421089 0.3419510 -5.446050e-01  0.78001422
#> [91,] -0.226947308 0.3211001 -8.460278e-01  0.41254751
#> [92,] -0.146005112 0.2234470 -5.771400e-01  0.29470521
#> [93,] -0.267397549 0.2825103 -8.338310e-01  0.28354674

# }