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] -0.1318704 10.2086065
variance(dist)
#> [1] 0.7280512 0.9053462
skewness(dist)
#> [1] -0.01112125 -0.09930075
generate(dist, 10)
#> [[1]]
#>  [1]  0.4854515  1.0536053  0.1606625 -1.4593433 -1.4593433 -0.9454977
#>  [7]  0.1606625 -0.7157040  0.5549964  0.3942178
#> 
#> [[2]]
#>  [1] 12.585788 10.466085  9.971005 10.152637  9.230631 10.136702 11.172371
#>  [8]  8.302808  8.748803 10.918669
#> 

density(dist, 1)
#> [1] 0.2535224 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,] 0.03211656 10.04109
variance(dist)
#>              x         y
#> [1,] 1.1348410 0.1288467
#> [2,] 0.1288467 0.8152757
generate(dist, 10)
#> [[1]]
#>                 x         y
#>  [1,] -1.50946969  8.842354
#>  [2,]  1.26495507  9.640448
#>  [3,] -0.75245662  9.985093
#>  [4,] -0.04287906  9.733883
#>  [5,]  0.17534740 10.143075
#>  [6,]  1.26495507  9.640448
#>  [7,]  0.51167018  9.726699
#>  [8,]  0.99175861  9.855505
#>  [9,]  0.51167018  9.726699
#> [10,] -0.40519219 10.231613
#> 
quantile(dist, 0.4) # Returns the marginal quantiles
#>               x        y
#> [1,] -0.1249546 9.817182
cdf(dist, matrix(c(0.3,9), nrow = 1))
#> [1] 0.37