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.
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_pareto1(),
tidy_pareto(),
tidy_t(),
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 -2.76 -3.86 0.000244 0.00287 -2.76
#> 2 1 2 0.528 -3.72 0.000751 0.701 0.528
#> 3 1 3 1.48 -3.57 0.00197 0.931 1.48
#> 4 1 4 0.788 -3.42 0.00439 0.785 0.788
#> 5 1 5 1.42 -3.27 0.00839 0.922 1.42
#> 6 1 6 -0.542 -3.13 0.0138 0.294 -0.542
#> 7 1 7 -1.72 -2.98 0.0198 0.0428 -1.72
#> 8 1 8 1.73 -2.83 0.0255 0.958 1.73
#> 9 1 9 -2.00 -2.68 0.0308 0.0228 -2.00
#> 10 1 10 0.181 -2.54 0.0372 0.572 0.181
#> # ℹ 40 more rows
