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 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.043 seconds (Warm-up)
#> Chain 1: 0.041 seconds (Sampling)
#> Chain 1: 0.084 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 8e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.08 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)
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#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
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#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.043 seconds (Warm-up)
#> Chain 2: 0.053 seconds (Sampling)
#> Chain 2: 0.096 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.11 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)
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#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
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#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.046 seconds (Warm-up)
#> Chain 3: 0.041 seconds (Sampling)
#> Chain 3: 0.087 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 8e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.08 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)
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#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
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#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.041 seconds (Warm-up)
#> Chain 4: 0.036 seconds (Sampling)
#> Chain 4: 0.077 seconds (Total)
#> Chain 4:
predictive_interval(fit)
#> 5% 95%
#> [1,] 1.00 8.00
#> [2,] 1.00 8.00
#> [3,] 1.00 7.00
#> [4,] 1.00 7.00
#> [5,] 8.00 19.00
#> [6,] 2.00 10.00
#> [7,] 1.00 8.00
#> [8,] 5.00 15.00
#> [9,] 2.00 9.00
#> [10,] 1.00 8.00
#> [11,] 5.00 16.00
#> [12,] 3.00 11.00
#> [13,] 1.00 9.00
#> [14,] 4.00 13.00
#> [15,] 14.00 29.00
#> [16,] 5.00 15.00
#> [17,] 2.00 9.00
#> [18,] 25.00 45.00
#> [19,] 2.00 9.00
#> [20,] 2.00 9.00
#> [21,] 1.00 8.00
#> [22,] 1.00 8.00
#> [23,] 2.00 9.00
#> [24,] 2.00 10.00
#> [25,] 6.00 16.00
#> [26,] 1.00 8.00
#> [27,] 1.00 8.00
#> [28,] 4.00 14.00
#> [29,] 10.00 23.05
#> [30,] 3.00 12.00
#> [31,] 2.00 9.00
#> [32,] 1.00 8.00
#> [33,] 2.00 9.00
#> [34,] 2.00 9.00
#> [35,] 3.00 11.00
#> [36,] 1.00 8.00
#> [37,] 1.00 8.00
#> [38,] 8.00 20.00
#> [39,] 3.00 13.00
#> [40,] 1.00 7.00
#> [41,] 2.00 9.00
#> [42,] 1.00 8.00
#> [43,] 4.00 14.00
#> [44,] 3.00 12.00
#> [45,] 3.00 12.00
#> [46,] 1.00 7.00
#> [47,] 3.00 12.00
#> [48,] 1.00 8.00
#> [49,] 66.00 99.00
#> [50,] 2.00 10.00
#> [51,] 3.00 13.00
#> [52,] 3.00 11.00
#> [53,] 6.00 17.00
#> [54,] 2.00 10.00
#> [55,] 1.00 8.00
#> [56,] 2.00 9.00
#> [57,] 2.00 10.00
#> [58,] 1.00 8.00
#> [59,] 1.00 8.00
#> [60,] 1.00 8.00
#> [61,] 1.00 8.00
#> [62,] 1.00 7.00
#> [63,] 1.00 7.00
#> [64,] 7.00 19.00
#> [65,] 2.00 10.00
#> [66,] 1.00 8.00
#> [67,] 5.00 15.00
#> [68,] 2.00 9.00
#> [69,] 1.00 8.00
#> [70,] 5.00 15.00
#> [71,] 3.00 11.00
#> [72,] 2.00 9.00
#> [73,] 4.00 13.00
#> [74,] 14.00 29.00
#> [75,] 5.00 15.00
#> [76,] 2.00 9.00
#> [77,] 25.00 45.00
#> [78,] 1.00 9.00
#> [79,] 2.00 9.00
#> [80,] 1.00 8.00
#> [81,] 1.00 7.00
#> [82,] 1.95 9.00
#> [83,] 2.00 10.00
#> [84,] 5.00 16.00
#> [85,] 1.00 8.00
#> [86,] 1.00 8.00
#> [87,] 4.00 14.00
#> [88,] 10.00 24.00
#> [89,] 3.00 12.00
#> [90,] 2.00 9.00
#> [91,] 1.00 7.05
#> [92,] 2.00 9.00
#> [93,] 2.00 10.00
#> [94,] 3.00 11.00
#> [95,] 1.00 8.00
#> [96,] 1.00 8.00
#> [97,] 8.00 20.00
#> [98,] 4.00 13.00
#> [99,] 1.00 7.00
#> [100,] 2.00 9.00
#> [101,] 1.00 8.00
#> [102,] 4.00 14.00
#> [103,] 3.00 12.00
#> [104,] 3.00 12.00
#> [105,] 1.00 7.00
#> [106,] 3.00 12.00
#> [107,] 1.00 8.00
#> [108,] 66.00 99.00
#> [109,] 2.00 9.00
#> [110,] 4.00 13.00
#> [111,] 3.00 11.00
#> [112,] 6.00 16.00
#> [113,] 2.00 10.00
#> [114,] 1.00 8.00
#> [115,] 2.00 9.00
#> [116,] 2.00 10.00
#> [117,] 1.00 8.00
#> [118,] 1.00 8.00
#> [119,] 1.00 8.00
#> [120,] 1.00 8.00
#> [121,] 1.00 7.00
#> [122,] 1.00 7.00
#> [123,] 8.00 20.00
#> [124,] 2.00 10.00
#> [125,] 1.00 8.00
#> [126,] 5.00 15.00
#> [127,] 2.00 9.00
#> [128,] 1.00 8.00
#> [129,] 5.00 16.00
#> [130,] 3.00 11.00
#> [131,] 2.00 9.00
#> [132,] 4.00 13.00
#> [133,] 14.00 29.00
#> [134,] 5.00 15.00
#> [135,] 2.00 9.00
#> [136,] 25.00 45.00
#> [137,] 2.00 9.00
#> [138,] 2.00 9.00
#> [139,] 1.00 8.00
#> [140,] 1.00 7.00
#> [141,] 1.00 8.00
#> [142,] 2.00 10.00
#> [143,] 6.00 16.00
#> [144,] 1.00 8.00
#> [145,] 1.00 8.00
#> [146,] 4.00 14.00
#> [147,] 10.00 23.05
#> [148,] 3.00 12.00
#> [149,] 2.00 9.00
#> [150,] 1.00 8.00
#> [151,] 2.00 9.00
#> [152,] 2.00 9.00
#> [153,] 3.00 11.00
#> [154,] 1.00 8.00
#> [155,] 1.00 8.00
#> [156,] 8.00 20.00
#> [157,] 4.00 13.00
#> [158,] 1.00 7.00
#> [159,] 2.00 9.00
#> [160,] 1.00 8.00
#> [161,] 4.00 14.00
#> [162,] 3.00 12.00
#> [163,] 3.00 12.00
#> [164,] 1.00 7.00
#> [165,] 3.00 12.00
#> [166,] 1.00 8.00
#> [167,] 66.00 99.00
#> [168,] 2.00 9.00
#> [169,] 4.00 13.00
#> [170,] 2.95 11.00
#> [171,] 6.00 17.00
#> [172,] 2.00 10.00
#> [173,] 1.00 9.00
#> [174,] 2.00 9.00
#> [175,] 2.00 10.00
#> [176,] 1.00 8.00
#> [177,] 1.00 8.00
#> [178,] 1.00 8.00
#> [179,] 1.00 8.00
#> [180,] 1.00 7.00
#> [181,] 1.00 7.00
#> [182,] 8.00 20.00
#> [183,] 2.00 10.00
#> [184,] 1.00 8.00
#> [185,] 5.00 15.00
#> [186,] 2.00 9.00
#> [187,] 1.00 8.00
#> [188,] 5.00 15.00
#> [189,] 3.00 11.00
#> [190,] 1.00 9.00
#> [191,] 4.00 13.00
#> [192,] 14.00 29.00
#> [193,] 5.00 15.00
#> [194,] 2.00 9.00
#> [195,] 25.00 45.00
#> [196,] 2.00 9.00
#> [197,] 2.00 9.00
#> [198,] 1.00 8.00
#> [199,] 1.00 8.00
#> [200,] 1.00 8.00
#> [201,] 2.00 10.00
#> [202,] 5.00 16.00
#> [203,] 1.00 8.00
#> [204,] 1.00 8.00
#> [205,] 4.00 14.00
#> [206,] 10.00 23.00
#> [207,] 3.00 12.00
#> [208,] 2.00 9.00
#> [209,] 1.00 8.00
#> [210,] 2.00 9.00
#> [211,] 2.00 10.00
#> [212,] 3.00 11.00
#> [213,] 1.00 8.00
#> [214,] 1.00 8.00
#> [215,] 8.00 20.00
#> [216,] 4.00 13.00
#> [217,] 1.00 7.00
#> [218,] 2.00 9.00
#> [219,] 1.00 8.00
#> [220,] 4.00 14.00
#> [221,] 3.00 12.00
#> [222,] 3.00 12.00
#> [223,] 1.00 7.00
#> [224,] 3.00 12.00
#> [225,] 1.00 8.00
#> [226,] 66.00 98.00
#> [227,] 2.00 9.00
#> [228,] 4.00 13.00
#> [229,] 3.00 11.00
#> [230,] 6.00 17.00
#> [231,] 2.00 10.00
#> [232,] 1.00 8.00
#> [233,] 2.00 9.00
#> [234,] 2.00 10.00
#> [235,] 1.00 8.00
#> [236,] 1.00 8.00
# }