This function will generate n random points from a Gaussian
distribution with a user provided, .mean, .sd - standard deviation and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresponds to the n randomly
generated points, the dnorm, pnorm and qnorm data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_numberThe current simulation number.xThe current value ofnfor the current simulation.yThe randomly generated data point.dxThexvalue from thestats::density()function.dyTheyvalue from thestats::density()function.pThe values from the resulting p_ function of the distribution family.qThe values from the resulting q_ function of the distribution family.
Arguments
- .n
The number of randomly generated points you want.
- .mean
The mean of the randomly generated data.
- .sd
The standard deviation of the randomly generated data.
- .num_sims
The number of randomly generated simulations you want.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
Details
This function uses the underlying stats::rnorm(), stats::pnorm(),
and stats::qnorm() functions to generate data from the given parameters. For
more information please see stats::rnorm()
See also
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Gaussian:
tidy_inverse_normal(),
util_normal_param_estimate(),
util_normal_stats_tbl()
Examples
tidy_normal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.920 -3.64 0.000224 0.821 0.920
#> 2 1 2 0.166 -3.50 0.000595 0.566 0.166
#> 3 1 3 0.0983 -3.37 0.00140 0.539 0.0983
#> 4 1 4 -0.231 -3.23 0.00293 0.409 -0.231
#> 5 1 5 -1.17 -3.09 0.00544 0.121 -1.17
#> 6 1 6 0.468 -2.95 0.00899 0.680 0.468
#> 7 1 7 -1.16 -2.81 0.0133 0.124 -1.16
#> 8 1 8 0.657 -2.68 0.0176 0.745 0.657
#> 9 1 9 -0.729 -2.54 0.0213 0.233 -0.729
#> 10 1 10 1.27 -2.40 0.0244 0.897 1.27
#> # ℹ 40 more rows
