Combine Multiple Tidy Distributions of Different Types
Source:R/combine-tidy-distributions-tbl.R
tidy_combine_distributions.RdThis allows a user to specify any n number of tidy_
distributions that can be combined into a single tibble. This is the preferred
method for combining multiple distributions of different types, for example
a Gaussian distribution and a Beta distribution.
This generates a single tibble with an added column of dist_type that will give the distribution family name and its associated parameters.
See also
Other Multiple Distribution:
tidy_multi_single_dist()
Examples
tn <- tidy_normal()
tb <- tidy_beta()
tc <- tidy_cauchy()
tidy_combine_distributions(tn, tb, tc)
#> # A tibble: 150 × 8
#> sim_number x y dx dy p q dist_type
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 1 1 0.623 -3.34 0.000289 0.734 0.623 Gaussian c(0, 1)
#> 2 1 2 0.461 -3.20 0.000969 0.678 0.461 Gaussian c(0, 1)
#> 3 1 3 0.549 -3.07 0.00268 0.709 0.549 Gaussian c(0, 1)
#> 4 1 4 2.24 -2.94 0.00615 0.987 2.24 Gaussian c(0, 1)
#> 5 1 5 -1.30 -2.80 0.0117 0.0965 -1.30 Gaussian c(0, 1)
#> 6 1 6 -1.01 -2.67 0.0184 0.156 -1.01 Gaussian c(0, 1)
#> 7 1 7 0.522 -2.54 0.0240 0.699 0.522 Gaussian c(0, 1)
#> 8 1 8 -0.197 -2.40 0.0260 0.422 -0.197 Gaussian c(0, 1)
#> 9 1 9 1.27 -2.27 0.0235 0.898 1.27 Gaussian c(0, 1)
#> 10 1 10 0.542 -2.14 0.0182 0.706 0.542 Gaussian c(0, 1)
#> # ℹ 140 more rows
## OR
tidy_combine_distributions(
tidy_normal(),
tidy_beta(),
tidy_cauchy(),
tidy_logistic()
)
#> # A tibble: 200 × 8
#> sim_number x y dx dy p q dist_type
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 1 1 -0.277 -3.57 0.000264 0.391 -0.277 Gaussian c(0, 1)
#> 2 1 2 -0.881 -3.43 0.000827 0.189 -0.881 Gaussian c(0, 1)
#> 3 1 3 0.733 -3.29 0.00219 0.768 0.733 Gaussian c(0, 1)
#> 4 1 4 -0.365 -3.15 0.00493 0.358 -0.365 Gaussian c(0, 1)
#> 5 1 5 -0.238 -3.02 0.00941 0.406 -0.238 Gaussian c(0, 1)
#> 6 1 6 -1.68 -2.88 0.0154 0.0467 -1.68 Gaussian c(0, 1)
#> 7 1 7 1.28 -2.74 0.0217 0.899 1.28 Gaussian c(0, 1)
#> 8 1 8 -0.736 -2.60 0.0273 0.231 -0.736 Gaussian c(0, 1)
#> 9 1 9 0.139 -2.46 0.0324 0.555 0.139 Gaussian c(0, 1)
#> 10 1 10 -0.160 -2.33 0.0390 0.436 -0.160 Gaussian c(0, 1)
#> # ℹ 190 more rows