Generate Multiple Tidy Distributions of a single type
Source:R/combine-multi-single-dist-tbl.R
tidy_multi_single_dist.RdGenerate multiple distributions of data from the same tidy_
distribution function.
Usage
tidy_multi_single_dist(.tidy_dist = NULL, .param_list = list())Arguments
- .tidy_dist
The type of
tidy_distribution that you want to run. You can only choose one.- .param_list
This must be a
list()object of the parameters that you want to pass through to the TidyDensitytidy_distribution function.
Details
Generate multiple distributions of data from the same tidy_
distribution function. This allows you to simulate multiple distributions of
the same family in order to view how shapes change with parameter changes. You
can then visualize the differences however you choose.
See also
Other Multiple Distribution:
tidy_combine_distributions()
Examples
tidy_multi_single_dist(
.tidy_dist = "tidy_normal",
.param_list = list(
.n = 50,
.mean = c(-1, 0, 1),
.sd = 1,
.num_sims = 3,
.return_tibble = TRUE
)
)
#> # A tibble: 450 × 8
#> sim_number dist_name x y dx dy p q
#> <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Gaussian c(-1, 1) 1 -1.00 -4.51 0.000224 0.499 -1.00
#> 2 1 Gaussian c(-1, 1) 2 -1.21 -4.37 0.000584 0.417 -1.21
#> 3 1 Gaussian c(-1, 1) 3 -1.55 -4.23 0.00136 0.291 -1.55
#> 4 1 Gaussian c(-1, 1) 4 -2.00 -4.10 0.00285 0.159 -2.00
#> 5 1 Gaussian c(-1, 1) 5 -0.998 -3.96 0.00537 0.501 -0.998
#> 6 1 Gaussian c(-1, 1) 6 -1.80 -3.82 0.00915 0.211 -1.80
#> 7 1 Gaussian c(-1, 1) 7 -0.949 -3.69 0.0143 0.520 -0.949
#> 8 1 Gaussian c(-1, 1) 8 -1.42 -3.55 0.0207 0.336 -1.42
#> 9 1 Gaussian c(-1, 1) 9 -2.30 -3.42 0.0288 0.0960 -2.30
#> 10 1 Gaussian c(-1, 1) 10 -1.35 -3.28 0.0392 0.365 -1.35
#> # ℹ 440 more rows
tidy_multi_single_dist(
.tidy_dist = "tidy_normal",
.param_list = list(
.n = 50,
.mean = c(-1, 0, 1),
.sd = 1,
.num_sims = 3,
.return_tibble = FALSE
)
)
#> sim_number dist_name x y dx dy
#> <fctr> <fctr> <int> <num> <num> <num>
#> 1: 1 Gaussian c(-1, 1) 1 -1.1246093 -4.795337 0.0002178942
#> 2: 1 Gaussian c(-1, 1) 2 -0.1464203 -4.641775 0.0006045876
#> 3: 1 Gaussian c(-1, 1) 3 -2.6360620 -4.488213 0.0014859375
#> 4: 1 Gaussian c(-1, 1) 4 -1.7831379 -4.334651 0.0032434431
#> 5: 1 Gaussian c(-1, 1) 5 -1.5017047 -4.181089 0.0063089364
#> ---
#> 446: 3 Gaussian c(1, 1) 46 -0.2268759 4.237218 0.0067100536
#> 447: 3 Gaussian c(1, 1) 47 0.5696987 4.375429 0.0035494345
#> 448: 3 Gaussian c(1, 1) 48 1.1117364 4.513640 0.0016472812
#> 449: 3 Gaussian c(1, 1) 49 -0.3629532 4.651851 0.0006702934
#> 450: 3 Gaussian c(1, 1) 50 -0.2865964 4.790063 0.0002390150
#> p q
#> <num> <num>
#> 1: 0.45041645 -1.1246093
#> 2: 0.80333106 -0.1464203
#> 3: 0.05091331 -2.6360620
#> 4: 0.21677307 -1.7831379
#> 5: 0.30793764 -1.5017047
#> ---
#> 446: 0.10993461 -0.2268759
#> 447: 0.33348825 0.5696987
#> 448: 0.54448378 1.1117364
#> 449: 0.08644864 -0.3629532
#> 450: 0.09911749 -0.2865964