Sampling distribution

dist_sample(x)

Arguments

x

A list of sampled values.

Examples

# Univariate numeric samples
dist <- dist_sample(x = list(rnorm(100), rnorm(100, 10)))

dist
#> <distribution[2]>
#> [1] sample[100] sample[100]
mean(dist)
#> [1] -0.1136047 10.0712533
variance(dist)
#> [1] 1.0129719 0.8350596
skewness(dist)
#> [1] 0.4029380 0.1892396
generate(dist, 10)
#> [[1]]
#>  [1]  0.1053733  0.3942178 -0.7238691  0.8250203 -0.9234880  0.2558145
#>  [7]  0.6866263  1.2292868  0.2330484 -0.8113330
#> 
#> [[2]]
#>  [1] 10.233440  9.527330  9.582240  9.200704 10.400266 10.918669 10.485451
#>  [8] 11.393081 11.130172 10.048189
#> 

density(dist, 1)
#> [1] 0.2018403 0.0000000

# Multivariate numeric samples
dist <- dist_sample(x = list(cbind(rnorm(100), rnorm(100, 10))))

dist
#> <distribution[1]>
#> [1] sample[100]
mean(dist)
#> [1]  0.05039244 10.07866275
variance(dist)
#>             [,1]        [,2]
#> [1,]  0.86300698 -0.08067302
#> [2,] -0.08067302  0.83666712
skewness(dist)
#> [1] -0.002453366
generate(dist, 10)
#> [[1]]
#>              [,1]      [,2]
#>  [1,] -0.57852490  9.291515
#>  [2,]  0.02880981 11.566691
#>  [3,] -1.30267167 10.362417
#>  [4,] -0.08603361  8.806459
#>  [5,]  0.23149277  9.718275
#>  [6,] -0.41092970 10.647418
#>  [7,] -0.09455546 11.384621
#>  [8,] -0.07257999 10.304062
#>  [9,] -0.28630015 10.238515
#> [10,] -0.76673461 10.398903
#> 

density(dist, 1)
#> [1] 0.2993009 0.0000000