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This function will generate n random points from a rt distribution with a user provided, df, ncp, 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 d_, p_ and q_ 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_t(.n = 50, .df = 1, .ncp = 0, .num_sims = 1)

Arguments

.n

The number of randomly generated points you want.

.df

Degrees of freedom, Inf is allowed.

.ncp

Non-centrality parameter.

.num_sims

The number of randomly generated simulations you want.

Value

A tibble of randomly generated data.

Details

This function uses the underlying stats::rt(), and its underlying p, d, and q functions. For more information please see stats::rt()

Author

Steven P. Sanderson II, MPH

Examples

tidy_t()
#> # A tibble: 50 × 7
#>    sim_number     x       y     dx       dy     p       q
#>    <fct>      <int>   <dbl>  <dbl>    <dbl> <dbl>   <dbl>
#>  1 1              1  2.08    -5.23 9.28e- 4 0.857  2.08  
#>  2 1              2  2.43     7.16 1.12e- 6 0.876  2.43  
#>  3 1              3  0.163   19.5  6.94e-11 0.551  0.163 
#>  4 1              4  0.801   31.9  0        0.715  0.801 
#>  5 1              5 -0.255   44.3  8.71e-19 0.421 -0.255 
#>  6 1              6 -1.12    56.7  2.70e-18 0.233 -1.12  
#>  7 1              7  0.0980  69.1  2.11e-18 0.531  0.0980
#>  8 1              8  3.11    81.5  0        0.901  3.11  
#>  9 1              9  2.67    93.8  0        0.886  2.67  
#> 10 1             10  0.453  106.   1.03e-18 0.636  0.453 
#> # … with 40 more rows