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] -0.07202645 10.08024671
variance(dist)
#> [1] 0.6775247 0.9756715
skewness(dist)
#> [1] 0.008113983 0.135510505
generate(dist, 10)
#> [[1]]
#>  [1]  0.5620251  0.1910260  0.8322193  0.2683600 -0.5882615  0.3672309
#>  [7]  1.6165187 -0.2254519 -0.4955087 -1.4673770
#> 
#> [[2]]
#>  [1]  9.439392  9.607291  7.437476 11.082792  8.754518 11.040919 11.040919
#>  [8] 13.077088  8.754518 12.098537
#> 

density(dist, 1)
#> [1] 0.2165326 0.0000000

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

dist
#> <distribution[1]>
#> [1] sample[100]
mean(dist)
#>               x       y
#> [1,] 0.03330121 9.97632
variance(dist)
#>              x         y
#> [1,] 1.2002489 0.1438919
#> [2,] 0.1438919 0.8788955
generate(dist, 10)
#> [[1]]
#>                 x         y
#>  [1,] -0.54043706 10.799526
#>  [2,]  0.22070920 10.616250
#>  [3,]  1.26495507  9.640448
#>  [4,]  0.06372166  9.809402
#>  [5,]  0.73367431 11.244005
#>  [6,]  1.02096351 10.353401
#>  [7,] -0.13567761 10.794906
#>  [8,]  2.90270571  8.694576
#>  [9,]  0.34454991 11.290768
#> [10,]  0.07640337 10.523857
#> 
quantile(dist, 0.4) # Returns the marginal quantiles
#>               x        y
#> [1,] -0.1249546 9.719756
cdf(dist, matrix(c(0.3,9), nrow = 1))
#> [1] 0.39