Skip to contents

Extract distributions from BirdFlow models

Source: R/get_distr.R
get_distr.Rd

get_distr will return one or more distributions in their flattened form. A single distribution will be returned as a vector; if multiple they will be columns in a matrix.

Usage

get_distr(
  x,
  which = "all",
  type = c("normalized", "marginal", "raw"),
  from_marginals
)

Arguments

x

A BirdFlow model

which

Indicates which timesteps to return. Can be one or more integers indicating timesteps; character dates in the format year-month-day e.g. "2019-02-25"; Date objects; or "all" which will return distributions for all timesteps.

type

One of "normalized" (the default), "marginal", or "raw". See "Distribution types" for details.

from_marginals

Deprecated. Use type = "marginal" instead. When supplied, from_marginals = TRUE is translated to type = "marginal" and from_marginals = FALSE to type = "normalized", with a warning.

Value

Either a vector with a distribution for a single timestep or a matrix with a column for each distribution.

Distribution types

The type argument controls how the distribution is computed:

"normalized"

The default. Returns the eBird-derived stored distributions, normalized so each timestep sums to 1. Equivalent to the previous behavior with from_marginals = FALSE.

"marginal"

Calculates the distribution from the marginals instead of the stored distributions. Useful for diagnostics; the two are very similar in practice. Equivalent to the previous behavior with from_marginals = TRUE.

"raw"

Returns the eBird abundance values prior to the standardize-to-1 normalization, by multiplying the stored normalized distribution by the per-timestep totals captured during preprocess_species() (x$metadata$abundance$totals). This requires a model that recorded those totals — older models will trigger an error pointing at re-preprocessing. Note that quantile trimming via the trim_quantile argument is also a lossy step: "raw" recovers values from before normalization but after any trimming.

See also

Distributions can be passed to predict() or converted to rasters with expand_distr() or converted to SpatRaster with rasterize_distr(). sample_distr() will convert one cell to 1 and the rest to 0 probabilistically based on the densities in the distribution.

Examples

# \donttest{
bf <- BirdFlowExamples::amewoo
get_distr(bf, which = 1) # first timestep, as a vector
#>   [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>   [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [36] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [41] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [46] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [51] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [56] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [61] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [66] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [71] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [76] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [81] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [86] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [91] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [96] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [101] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [106] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [111] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [116] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [121] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [126] 0.000000e+00 2.654108e-06 9.882774e-04 3.971355e-03 0.000000e+00
#> [131] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [136] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [141] 0.000000e+00 0.000000e+00 5.919668e-07 4.710217e-04 4.330653e-04
#> [146] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [151] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [156] 0.000000e+00 0.000000e+00 3.464510e-06 1.961725e-03 6.931312e-03
#> [161] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [166] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [171] 7.508743e-06 2.957621e-03 8.718165e-03 4.529720e-03 0.000000e+00
#> [176] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [181] 2.482801e-05 2.028553e-04 9.213331e-07 0.000000e+00 0.000000e+00
#> [186] 2.838783e-03 4.215964e-02 4.163246e-02 6.921357e-03 0.000000e+00
#> [191] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [196] 3.273664e-03 8.458369e-03 6.163124e-03 1.959965e-03 4.947349e-04
#> [201] 3.204921e-02 4.898101e-02 4.168029e-02 1.578872e-02 0.000000e+00
#> [206] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.752682e-04
#> [211] 1.400020e-02 1.569149e-02 2.634848e-02 1.136832e-02 2.790600e-02
#> [216] 5.111686e-02 3.304982e-02 1.808712e-02 2.444598e-03 6.770740e-06
#> [221] 1.619776e-05 8.352012e-05 1.513218e-03 4.433005e-04 2.247404e-03
#> [226] 1.239867e-02 1.664490e-02 4.570713e-02 2.850836e-02 2.633825e-02
#> [231] 3.541156e-02 1.931686e-02 5.252171e-04 5.706915e-04 4.045021e-03
#> [236] 1.625290e-02 6.044832e-03 1.712079e-04 5.228329e-03 1.918284e-02
#> [241] 5.111686e-02 1.977860e-02 1.168555e-02 1.966511e-02 4.374494e-02
#> [246] 0.000000e+00 3.067777e-04 9.318547e-04 9.440010e-03 1.819547e-02
#> [251] 7.850784e-03 1.659561e-03 1.632366e-03 7.892153e-03 1.439658e-02
#> [256] 5.863142e-03 2.924571e-03 1.240335e-03 3.411255e-06 6.777501e-04
#> [261] 4.919647e-03 7.542769e-03 9.197147e-03 9.636015e-03 3.672142e-03
#> [266] 7.859617e-04 1.457520e-04 1.932125e-03 6.706695e-03 1.859059e-03
#> [271] 1.894553e-03 1.634350e-07 8.295965e-05 1.789877e-03 3.678946e-03
#> [276] 1.492053e-04 3.081023e-04 4.024127e-05 6.266951e-04 1.115313e-03
#> [281] 2.556100e-04 2.410929e-06 9.835884e-07
#> attr(,"time")
#> [1] "January 4"
get_distr(bf, which = c(1, 2)) # first two timesteps, as a matrix
#>         time
#> i           January 4   January 11
#>     [1,] 0.000000e+00 0.000000e+00
#>     [2,] 0.000000e+00 0.000000e+00
#>     [3,] 0.000000e+00 0.000000e+00
#>     [4,] 0.000000e+00 0.000000e+00
#>     [5,] 0.000000e+00 0.000000e+00
#>     [6,] 0.000000e+00 0.000000e+00
#>     [7,] 0.000000e+00 0.000000e+00
#>     [8,] 0.000000e+00 0.000000e+00
#>     [9,] 0.000000e+00 0.000000e+00
#>    [10,] 0.000000e+00 0.000000e+00
#>    [11,] 0.000000e+00 0.000000e+00
#>    [12,] 0.000000e+00 0.000000e+00
#>    [13,] 0.000000e+00 0.000000e+00
#>    [14,] 0.000000e+00 0.000000e+00
#>    [15,] 0.000000e+00 0.000000e+00
#>    [16,] 0.000000e+00 0.000000e+00
#>    [17,] 0.000000e+00 0.000000e+00
#>    [18,] 0.000000e+00 0.000000e+00
#>    [19,] 0.000000e+00 0.000000e+00
#>    [20,] 0.000000e+00 0.000000e+00
#>    [21,] 0.000000e+00 0.000000e+00
#>    [22,] 0.000000e+00 0.000000e+00
#>    [23,] 0.000000e+00 0.000000e+00
#>    [24,] 0.000000e+00 0.000000e+00
#>    [25,] 0.000000e+00 0.000000e+00
#>    [26,] 0.000000e+00 0.000000e+00
#>    [27,] 0.000000e+00 0.000000e+00
#>    [28,] 0.000000e+00 0.000000e+00
#>    [29,] 0.000000e+00 0.000000e+00
#>    [30,] 0.000000e+00 0.000000e+00
#>    [31,] 0.000000e+00 0.000000e+00
#>    [32,] 0.000000e+00 0.000000e+00
#>    [33,] 0.000000e+00 0.000000e+00
#>    [34,] 0.000000e+00 0.000000e+00
#>    [35,] 0.000000e+00 0.000000e+00
#>    [36,] 0.000000e+00 0.000000e+00
#>    [37,] 0.000000e+00 0.000000e+00
#>    [38,] 0.000000e+00 0.000000e+00
#>    [39,] 0.000000e+00 0.000000e+00
#>    [40,] 0.000000e+00 0.000000e+00
#>    [41,] 0.000000e+00 0.000000e+00
#>    [42,] 0.000000e+00 0.000000e+00
#>    [43,] 0.000000e+00 0.000000e+00
#>    [44,] 0.000000e+00 0.000000e+00
#>    [45,] 0.000000e+00 0.000000e+00
#>    [46,] 0.000000e+00 0.000000e+00
#>    [47,] 0.000000e+00 0.000000e+00
#>    [48,] 0.000000e+00 0.000000e+00
#>    [49,] 0.000000e+00 0.000000e+00
#>    [50,] 0.000000e+00 0.000000e+00
#>    [51,] 0.000000e+00 0.000000e+00
#>    [52,] 0.000000e+00 0.000000e+00
#>    [53,] 0.000000e+00 0.000000e+00
#>    [54,] 0.000000e+00 0.000000e+00
#>    [55,] 0.000000e+00 0.000000e+00
#>    [56,] 0.000000e+00 0.000000e+00
#>    [57,] 0.000000e+00 0.000000e+00
#>    [58,] 0.000000e+00 0.000000e+00
#>    [59,] 0.000000e+00 0.000000e+00
#>    [60,] 0.000000e+00 0.000000e+00
#>    [61,] 0.000000e+00 0.000000e+00
#>    [62,] 0.000000e+00 0.000000e+00
#>    [63,] 0.000000e+00 0.000000e+00
#>    [64,] 0.000000e+00 0.000000e+00
#>    [65,] 0.000000e+00 0.000000e+00
#>    [66,] 0.000000e+00 0.000000e+00
#>    [67,] 0.000000e+00 0.000000e+00
#>    [68,] 0.000000e+00 0.000000e+00
#>    [69,] 0.000000e+00 0.000000e+00
#>    [70,] 0.000000e+00 0.000000e+00
#>    [71,] 0.000000e+00 0.000000e+00
#>    [72,] 0.000000e+00 0.000000e+00
#>    [73,] 0.000000e+00 0.000000e+00
#>    [74,] 0.000000e+00 0.000000e+00
#>    [75,] 0.000000e+00 0.000000e+00
#>    [76,] 0.000000e+00 0.000000e+00
#>    [77,] 0.000000e+00 0.000000e+00
#>    [78,] 0.000000e+00 0.000000e+00
#>    [79,] 0.000000e+00 0.000000e+00
#>    [80,] 0.000000e+00 0.000000e+00
#>    [81,] 0.000000e+00 0.000000e+00
#>    [82,] 0.000000e+00 0.000000e+00
#>    [83,] 0.000000e+00 0.000000e+00
#>    [84,] 0.000000e+00 0.000000e+00
#>    [85,] 0.000000e+00 0.000000e+00
#>    [86,] 0.000000e+00 0.000000e+00
#>    [87,] 0.000000e+00 0.000000e+00
#>    [88,] 0.000000e+00 0.000000e+00
#>    [89,] 0.000000e+00 0.000000e+00
#>    [90,] 0.000000e+00 0.000000e+00
#>    [91,] 0.000000e+00 0.000000e+00
#>    [92,] 0.000000e+00 0.000000e+00
#>    [93,] 0.000000e+00 0.000000e+00
#>    [94,] 0.000000e+00 0.000000e+00
#>    [95,] 0.000000e+00 0.000000e+00
#>    [96,] 0.000000e+00 0.000000e+00
#>    [97,] 0.000000e+00 0.000000e+00
#>    [98,] 0.000000e+00 0.000000e+00
#>    [99,] 0.000000e+00 0.000000e+00
#>   [100,] 0.000000e+00 0.000000e+00
#>   [101,] 0.000000e+00 0.000000e+00
#>   [102,] 0.000000e+00 0.000000e+00
#>   [103,] 0.000000e+00 0.000000e+00
#>   [104,] 0.000000e+00 0.000000e+00
#>   [105,] 0.000000e+00 0.000000e+00
#>   [106,] 0.000000e+00 0.000000e+00
#>   [107,] 0.000000e+00 0.000000e+00
#>   [108,] 0.000000e+00 0.000000e+00
#>   [109,] 0.000000e+00 0.000000e+00
#>   [110,] 0.000000e+00 0.000000e+00
#>   [111,] 0.000000e+00 0.000000e+00
#>   [112,] 0.000000e+00 0.000000e+00
#>   [113,] 0.000000e+00 0.000000e+00
#>   [114,] 0.000000e+00 0.000000e+00
#>   [115,] 0.000000e+00 0.000000e+00
#>   [116,] 0.000000e+00 0.000000e+00
#>   [117,] 0.000000e+00 0.000000e+00
#>   [118,] 0.000000e+00 0.000000e+00
#>   [119,] 0.000000e+00 0.000000e+00
#>   [120,] 0.000000e+00 0.000000e+00
#>   [121,] 0.000000e+00 0.000000e+00
#>   [122,] 0.000000e+00 0.000000e+00
#>   [123,] 0.000000e+00 0.000000e+00
#>   [124,] 0.000000e+00 0.000000e+00
#>   [125,] 0.000000e+00 0.000000e+00
#>   [126,] 0.000000e+00 0.000000e+00
#>   [127,] 2.654108e-06 3.565523e-07
#>   [128,] 9.882774e-04 8.889533e-04
#>   [129,] 3.971355e-03 2.056128e-03
#>   [130,] 0.000000e+00 0.000000e+00
#>   [131,] 0.000000e+00 0.000000e+00
#>   [132,] 0.000000e+00 0.000000e+00
#>   [133,] 0.000000e+00 0.000000e+00
#>   [134,] 0.000000e+00 0.000000e+00
#>   [135,] 0.000000e+00 0.000000e+00
#>   [136,] 0.000000e+00 0.000000e+00
#>   [137,] 0.000000e+00 0.000000e+00
#>   [138,] 0.000000e+00 0.000000e+00
#>   [139,] 0.000000e+00 0.000000e+00
#>   [140,] 0.000000e+00 0.000000e+00
#>   [141,] 0.000000e+00 0.000000e+00
#>   [142,] 0.000000e+00 0.000000e+00
#>   [143,] 5.919668e-07 1.197562e-06
#>   [144,] 4.710217e-04 3.614362e-04
#>   [145,] 4.330653e-04 1.029358e-04
#>   [146,] 0.000000e+00 0.000000e+00
#>   [147,] 0.000000e+00 0.000000e+00
#>   [148,] 0.000000e+00 0.000000e+00
#>   [149,] 0.000000e+00 0.000000e+00
#>   [150,] 0.000000e+00 0.000000e+00
#>   [151,] 0.000000e+00 0.000000e+00
#>   [152,] 0.000000e+00 0.000000e+00
#>   [153,] 0.000000e+00 0.000000e+00
#>   [154,] 0.000000e+00 0.000000e+00
#>   [155,] 0.000000e+00 0.000000e+00
#>   [156,] 0.000000e+00 0.000000e+00
#>   [157,] 0.000000e+00 0.000000e+00
#>   [158,] 3.464510e-06 9.033894e-07
#>   [159,] 1.961725e-03 8.380408e-04
#>   [160,] 6.931312e-03 5.354991e-03
#>   [161,] 0.000000e+00 0.000000e+00
#>   [162,] 0.000000e+00 0.000000e+00
#>   [163,] 0.000000e+00 0.000000e+00
#>   [164,] 0.000000e+00 0.000000e+00
#>   [165,] 0.000000e+00 0.000000e+00
#>   [166,] 0.000000e+00 0.000000e+00
#>   [167,] 0.000000e+00 0.000000e+00
#>   [168,] 0.000000e+00 0.000000e+00
#>   [169,] 0.000000e+00 0.000000e+00
#>   [170,] 0.000000e+00 0.000000e+00
#>   [171,] 7.508743e-06 3.100107e-05
#>   [172,] 2.957621e-03 2.969601e-03
#>   [173,] 8.718165e-03 5.019109e-03
#>   [174,] 4.529720e-03 2.176861e-03
#>   [175,] 0.000000e+00 0.000000e+00
#>   [176,] 0.000000e+00 0.000000e+00
#>   [177,] 0.000000e+00 0.000000e+00
#>   [178,] 0.000000e+00 0.000000e+00
#>   [179,] 0.000000e+00 0.000000e+00
#>   [180,] 0.000000e+00 0.000000e+00
#>   [181,] 2.482801e-05 6.310766e-06
#>   [182,] 2.028553e-04 1.188651e-04
#>   [183,] 9.213331e-07 2.263695e-08
#>   [184,] 0.000000e+00 0.000000e+00
#>   [185,] 0.000000e+00 0.000000e+00
#>   [186,] 2.838783e-03 3.268596e-03
#>   [187,] 4.215964e-02 4.019933e-02
#>   [188,] 4.163246e-02 3.079380e-02
#>   [189,] 6.921357e-03 3.714015e-03
#>   [190,] 0.000000e+00 0.000000e+00
#>   [191,] 0.000000e+00 0.000000e+00
#>   [192,] 0.000000e+00 0.000000e+00
#>   [193,] 0.000000e+00 0.000000e+00
#>   [194,] 0.000000e+00 0.000000e+00
#>   [195,] 0.000000e+00 0.000000e+00
#>   [196,] 3.273664e-03 1.553879e-03
#>   [197,] 8.458369e-03 6.373524e-03
#>   [198,] 6.163124e-03 4.536790e-03
#>   [199,] 1.959965e-03 2.504159e-03
#>   [200,] 4.947349e-04 6.423733e-04
#>   [201,] 3.204921e-02 3.655231e-02
#>   [202,] 4.898101e-02 5.291559e-02
#>   [203,] 4.168029e-02 3.091268e-02
#>   [204,] 1.578872e-02 9.373600e-03
#>   [205,] 0.000000e+00 0.000000e+00
#>   [206,] 0.000000e+00 0.000000e+00
#>   [207,] 0.000000e+00 0.000000e+00
#>   [208,] 0.000000e+00 0.000000e+00
#>   [209,] 0.000000e+00 0.000000e+00
#>   [210,] 3.752682e-04 1.835994e-04
#>   [211,] 1.400020e-02 8.464431e-03
#>   [212,] 1.569149e-02 1.329234e-02
#>   [213,] 2.634848e-02 3.259160e-02
#>   [214,] 1.136832e-02 1.426787e-02
#>   [215,] 2.790600e-02 3.000402e-02
#>   [216,] 5.111686e-02 5.879032e-02
#>   [217,] 3.304982e-02 4.086008e-02
#>   [218,] 1.808712e-02 1.874369e-02
#>   [219,] 2.444598e-03 1.744748e-03
#>   [220,] 6.770740e-06 1.062538e-06
#>   [221,] 1.619776e-05 7.576586e-06
#>   [222,] 8.352012e-05 5.088027e-05
#>   [223,] 1.513218e-03 1.356882e-03
#>   [224,] 4.433005e-04 1.036324e-03
#>   [225,] 2.247404e-03 2.982708e-03
#>   [226,] 1.239867e-02 1.365319e-02
#>   [227,] 1.664490e-02 1.725833e-02
#>   [228,] 4.570713e-02 6.477607e-02
#>   [229,] 2.850836e-02 4.159356e-02
#>   [230,] 2.633825e-02 3.090403e-02
#>   [231,] 3.541156e-02 3.783831e-02
#>   [232,] 1.931686e-02 2.081285e-02
#>   [233,] 5.252171e-04 2.238520e-04
#>   [234,] 5.706915e-04 3.197031e-04
#>   [235,] 4.045021e-03 3.126662e-03
#>   [236,] 1.625290e-02 1.433055e-02
#>   [237,] 6.044832e-03 6.873423e-03
#>   [238,] 1.712079e-04 2.894906e-03
#>   [239,] 5.228329e-03 1.851514e-02
#>   [240,] 1.918284e-02 2.358787e-02
#>   [241,] 5.111686e-02 6.494330e-02
#>   [242,] 1.977860e-02 2.638768e-02
#>   [243,] 1.168555e-02 1.218678e-02
#>   [244,] 1.966511e-02 1.574598e-02
#>   [245,] 4.374494e-02 2.175522e-02
#>   [246,] 0.000000e+00 0.000000e+00
#>   [247,] 3.067777e-04 3.093264e-04
#>   [248,] 9.318547e-04 4.330130e-04
#>   [249,] 9.440010e-03 8.222507e-03
#>   [250,] 1.819547e-02 1.551629e-02
#>   [251,] 7.850784e-03 6.433329e-03
#>   [252,] 1.659561e-03 3.149134e-03
#>   [253,] 1.632366e-03 6.208958e-03
#>   [254,] 7.892153e-03 7.947623e-03
#>   [255,] 1.439658e-02 1.337951e-02
#>   [256,] 5.863142e-03 3.306289e-03
#>   [257,] 2.924571e-03 1.413636e-03
#>   [258,] 1.240335e-03 1.129026e-03
#>   [259,] 3.411255e-06 7.722040e-08
#>   [260,] 6.777501e-04 8.320473e-04
#>   [261,] 4.919647e-03 1.996955e-03
#>   [262,] 7.542769e-03 4.097796e-03
#>   [263,] 9.197147e-03 3.662867e-03
#>   [264,] 9.636015e-03 4.928882e-03
#>   [265,] 3.672142e-03 1.608119e-03
#>   [266,] 7.859617e-04 9.518181e-04
#>   [267,] 1.457520e-04 2.050694e-04
#>   [268,] 1.932125e-03 5.349919e-04
#>   [269,] 6.706695e-03 2.851272e-03
#>   [270,] 1.859059e-03 6.427025e-04
#>   [271,] 1.894553e-03 1.324302e-03
#>   [272,] 1.634350e-07 0.000000e+00
#>   [273,] 8.295965e-05 2.944419e-05
#>   [274,] 1.789877e-03 9.650077e-04
#>   [275,] 3.678946e-03 1.395035e-03
#>   [276,] 1.492053e-04 0.000000e+00
#>   [277,] 3.081023e-04 6.264322e-05
#>   [278,] 4.024127e-05 5.005799e-06
#>   [279,] 6.266951e-04 3.912671e-04
#>   [280,] 1.115313e-03 6.922596e-04
#>   [281,] 2.556100e-04 9.207028e-07
#>   [282,] 2.410929e-06 0.000000e+00
#>   [283,] 9.835884e-07 0.000000e+00
get_distr(bf, which = 1, type = "marginal") # calculated from marginals
#>   [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>   [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [36] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [41] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [46] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [51] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [56] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [61] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [66] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [71] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [76] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [81] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [86] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [91] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#>  [96] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [101] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [106] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [111] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [116] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [121] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [126] 0.000000e+00 5.712957e-06 1.938030e-05 5.618343e-05 0.000000e+00
#> [131] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [136] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [141] 0.000000e+00 0.000000e+00 1.655073e-05 3.844224e-05 1.642948e-05
#> [146] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [151] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [156] 0.000000e+00 0.000000e+00 2.529116e-03 2.929397e-05 7.687089e-03
#> [161] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [166] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [171] 1.654421e-03 4.466597e-03 7.909207e-03 5.228954e-03 0.000000e+00
#> [176] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [181] 9.470699e-05 3.632489e-03 4.202366e-05 0.000000e+00 0.000000e+00
#> [186] 2.834473e-03 4.182710e-02 3.651263e-02 5.177069e-03 0.000000e+00
#> [191] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [196] 4.895456e-03 9.939969e-03 9.635493e-03 3.884379e-03 4.516998e-04
#> [201] 3.298510e-02 4.790965e-02 3.516560e-02 9.888150e-03 0.000000e+00
#> [206] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.667564e-03
#> [211] 1.551560e-02 1.726562e-02 3.214395e-02 1.356184e-02 2.743978e-02
#> [216] 5.061887e-02 3.179893e-02 1.393373e-02 1.204408e-03 6.439394e-06
#> [221] 3.843160e-05 2.901124e-05 4.850045e-03 5.415572e-03 8.146671e-03
#> [226] 1.812845e-02 1.970875e-02 5.372360e-02 3.238841e-02 2.565110e-02
#> [231] 3.122648e-02 1.335717e-02 4.291130e-05 2.366876e-05 5.856264e-03
#> [236] 1.698592e-02 9.821521e-03 5.626639e-03 1.467991e-02 2.411125e-02
#> [241] 5.689824e-02 2.163615e-02 8.753137e-03 1.264700e-02 3.277251e-02
#> [246] 0.000000e+00 1.972824e-05 6.136136e-05 9.667543e-03 1.659504e-02
#> [251] 7.988423e-03 4.088854e-03 5.862602e-03 9.683562e-03 1.443285e-02
#> [256] 3.442015e-03 1.357871e-04 4.071566e-05 1.544311e-06 7.287152e-06
#> [261] 2.862087e-03 4.743645e-03 4.460301e-03 5.661770e-03 1.385165e-03
#> [266] 2.230937e-04 1.027184e-04 1.491397e-05 3.061313e-03 2.371033e-05
#> [271] 3.245825e-05 1.222136e-05 6.923670e-06 4.164838e-05 3.503858e-05
#> [276] 2.100202e-05 4.974213e-06 2.342136e-05 1.001783e-05 1.898884e-05
#> [281] 1.933296e-06 2.272615e-06 1.422841e-05
#> attr(,"time")
#> [1] "January 4"
# }