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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_number The current simulation number.

  • x The current value of n for the current simulation.

  • y The randomly generated data point.

  • dx The x value from the stats::density() function.

  • dy The y value from the stats::density() function.

  • p The values from the resulting p_ function of the distribution family.

  • q The values from the resulting q_ function of the distribution family.

Usage

tidy_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1)

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.

Value

A tibble of randomly generated data.

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()

Author

Steven P. Sanderson II, MPH

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