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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 -0.0198 -3.15 0.000313 0.492  -0.0198
#>  2 1              2 -0.342  -3.02 0.000810 0.366  -0.342 
#>  3 1              3 -1.52   -2.89 0.00191  0.0640 -1.52  
#>  4 1              4 -1.22   -2.76 0.00413  0.110  -1.22  
#>  5 1              5  0.459  -2.63 0.00816  0.677   0.459 
#>  6 1              6  0.205  -2.50 0.0148   0.581   0.205 
#>  7 1              7 -0.720  -2.37 0.0248   0.236  -0.720 
#>  8 1              8 -0.987  -2.24 0.0386   0.162  -0.987 
#>  9 1              9  1.35   -2.11 0.0559   0.911   1.35  
#> 10 1             10 -1.60   -1.98 0.0762   0.0543 -1.60  
#> # ℹ 40 more rows