Compute intervals from the posterior predictive distribution.
Arguments
- object
An R object of class
brmsfit.- prob
A number p (0 < p < 1) indicating the desired probability mass to include in the intervals. Defaults to
0.9.- ...
Further arguments passed to
posterior_predict.
Value
A matrix with 2 columns for the lower and upper bounds of the intervals, respectively, and as many rows as observations being predicted.
Examples
# \dontrun{
fit <- brm(count ~ zBase, data = epilepsy, family = poisson())
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.047 seconds (Warm-up)
#> Chain 1: 0.046 seconds (Sampling)
#> Chain 1: 0.093 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.046 seconds (Warm-up)
#> Chain 2: 0.043 seconds (Sampling)
#> Chain 2: 0.089 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
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#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.045 seconds (Warm-up)
#> Chain 3: 0.045 seconds (Sampling)
#> Chain 3: 0.09 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.044 seconds (Warm-up)
#> Chain 4: 0.04 seconds (Sampling)
#> Chain 4: 0.084 seconds (Total)
#> Chain 4:
predictive_interval(fit)
#> 5% 95%
#> [1,] 1 8.00
#> [2,] 1 8.00
#> [3,] 1 7.00
#> [4,] 1 7.00
#> [5,] 8 19.00
#> [6,] 2 10.00
#> [7,] 1 8.00
#> [8,] 5 15.00
#> [9,] 2 9.00
#> [10,] 1 8.00
#> [11,] 5 16.00
#> [12,] 3 11.00
#> [13,] 1 9.00
#> [14,] 4 13.00
#> [15,] 14 29.00
#> [16,] 5 15.00
#> [17,] 1 9.00
#> [18,] 25 45.00
#> [19,] 2 9.00
#> [20,] 2 9.00
#> [21,] 1 8.00
#> [22,] 1 8.00
#> [23,] 2 9.00
#> [24,] 2 10.00
#> [25,] 6 16.00
#> [26,] 1 8.00
#> [27,] 1 8.00
#> [28,] 4 14.00
#> [29,] 10 23.00
#> [30,] 3 12.00
#> [31,] 2 9.00
#> [32,] 1 8.00
#> [33,] 2 9.00
#> [34,] 2 9.00
#> [35,] 3 11.00
#> [36,] 1 8.00
#> [37,] 1 8.00
#> [38,] 8 20.00
#> [39,] 3 13.00
#> [40,] 1 7.00
#> [41,] 2 9.00
#> [42,] 1 8.00
#> [43,] 4 14.00
#> [44,] 3 12.00
#> [45,] 3 12.00
#> [46,] 1 7.00
#> [47,] 3 12.00
#> [48,] 1 8.00
#> [49,] 66 98.00
#> [50,] 2 10.00
#> [51,] 3 13.00
#> [52,] 3 11.00
#> [53,] 6 17.00
#> [54,] 2 10.00
#> [55,] 1 8.00
#> [56,] 2 9.00
#> [57,] 2 10.00
#> [58,] 1 8.00
#> [59,] 1 8.00
#> [60,] 1 8.00
#> [61,] 1 8.00
#> [62,] 1 7.00
#> [63,] 1 7.00
#> [64,] 8 20.00
#> [65,] 2 10.00
#> [66,] 1 8.00
#> [67,] 5 15.00
#> [68,] 2 9.00
#> [69,] 1 8.00
#> [70,] 5 15.00
#> [71,] 3 11.00
#> [72,] 1 9.00
#> [73,] 4 13.00
#> [74,] 14 29.00
#> [75,] 5 15.00
#> [76,] 2 9.00
#> [77,] 25 45.00
#> [78,] 1 9.00
#> [79,] 2 9.00
#> [80,] 1 8.00
#> [81,] 1 7.00
#> [82,] 1 9.00
#> [83,] 2 10.00
#> [84,] 5 16.00
#> [85,] 1 7.00
#> [86,] 1 8.00
#> [87,] 4 14.00
#> [88,] 10 24.00
#> [89,] 3 12.00
#> [90,] 2 9.00
#> [91,] 1 8.00
#> [92,] 2 9.00
#> [93,] 2 10.00
#> [94,] 3 11.00
#> [95,] 1 8.00
#> [96,] 1 8.00
#> [97,] 8 20.00
#> [98,] 4 13.00
#> [99,] 1 7.00
#> [100,] 2 9.00
#> [101,] 1 8.00
#> [102,] 4 14.00
#> [103,] 3 12.00
#> [104,] 3 12.00
#> [105,] 1 7.00
#> [106,] 3 12.00
#> [107,] 1 8.00
#> [108,] 67 98.00
#> [109,] 2 9.00
#> [110,] 4 13.00
#> [111,] 3 11.00
#> [112,] 6 16.00
#> [113,] 2 10.00
#> [114,] 1 8.00
#> [115,] 2 9.00
#> [116,] 2 10.00
#> [117,] 1 8.00
#> [118,] 1 8.00
#> [119,] 1 8.00
#> [120,] 1 8.00
#> [121,] 1 7.00
#> [122,] 1 7.00
#> [123,] 8 20.00
#> [124,] 2 10.00
#> [125,] 1 8.00
#> [126,] 5 15.00
#> [127,] 2 9.00
#> [128,] 1 8.00
#> [129,] 5 16.00
#> [130,] 3 11.00
#> [131,] 2 9.00
#> [132,] 4 13.00
#> [133,] 14 29.00
#> [134,] 5 15.00
#> [135,] 2 9.00
#> [136,] 25 45.00
#> [137,] 2 9.00
#> [138,] 2 9.00
#> [139,] 1 8.00
#> [140,] 1 7.00
#> [141,] 1 8.00
#> [142,] 2 10.00
#> [143,] 6 16.00
#> [144,] 1 8.00
#> [145,] 1 7.00
#> [146,] 4 14.00
#> [147,] 10 23.00
#> [148,] 3 12.00
#> [149,] 2 9.00
#> [150,] 1 8.00
#> [151,] 2 9.00
#> [152,] 2 9.00
#> [153,] 3 11.00
#> [154,] 1 8.00
#> [155,] 1 8.00
#> [156,] 8 20.00
#> [157,] 4 13.00
#> [158,] 1 7.00
#> [159,] 2 9.00
#> [160,] 1 8.00
#> [161,] 4 14.00
#> [162,] 3 12.00
#> [163,] 3 12.00
#> [164,] 1 7.00
#> [165,] 3 12.00
#> [166,] 1 8.00
#> [167,] 66 99.00
#> [168,] 2 9.00
#> [169,] 4 13.00
#> [170,] 3 11.00
#> [171,] 6 17.00
#> [172,] 2 10.00
#> [173,] 1 8.00
#> [174,] 2 9.00
#> [175,] 2 10.00
#> [176,] 1 8.00
#> [177,] 1 8.00
#> [178,] 1 8.00
#> [179,] 1 8.00
#> [180,] 1 7.00
#> [181,] 1 7.00
#> [182,] 8 20.00
#> [183,] 2 10.00
#> [184,] 1 8.00
#> [185,] 5 15.00
#> [186,] 2 9.00
#> [187,] 1 8.00
#> [188,] 5 15.00
#> [189,] 3 11.00
#> [190,] 1 9.00
#> [191,] 4 13.00
#> [192,] 14 29.00
#> [193,] 5 15.00
#> [194,] 2 8.05
#> [195,] 25 45.00
#> [196,] 2 9.00
#> [197,] 2 9.00
#> [198,] 1 8.00
#> [199,] 1 8.00
#> [200,] 1 8.00
#> [201,] 2 10.00
#> [202,] 6 16.00
#> [203,] 1 8.00
#> [204,] 1 8.00
#> [205,] 4 14.00
#> [206,] 10 24.00
#> [207,] 3 12.00
#> [208,] 2 9.00
#> [209,] 1 8.00
#> [210,] 2 9.00
#> [211,] 2 10.00
#> [212,] 3 11.00
#> [213,] 1 8.00
#> [214,] 1 8.00
#> [215,] 8 20.00
#> [216,] 4 13.00
#> [217,] 1 7.00
#> [218,] 2 9.00
#> [219,] 1 8.00
#> [220,] 4 14.00
#> [221,] 3 12.00
#> [222,] 3 12.00
#> [223,] 1 7.00
#> [224,] 3 12.00
#> [225,] 1 8.00
#> [226,] 66 98.00
#> [227,] 2 9.00
#> [228,] 4 13.00
#> [229,] 3 11.00
#> [230,] 6 17.00
#> [231,] 2 10.00
#> [232,] 1 8.00
#> [233,] 2 9.00
#> [234,] 2 10.00
#> [235,] 1 8.00
#> [236,] 1 8.00
# }