Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal • TidyDensity
Description: This function will generate n random points from a lognormal distribution with a user provided, .meanlog, .sdlog, 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.

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# Tidy Randomly Generated Lognormal Distribution Tibble

Source: `R/random-tidy-lognormal.R`
`tidy_lognormal.Rd`

This function will generate `n` random points from a lognormal distribution with a user provided, `.meanlog`, `.sdlog`, 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_lognormal(.n = 50, .meanlog = 0, .sdlog = 1, .num_sims = 1)

## Arguments

.n

  The number of randomly generated points you want.

.meanlog

  Mean of the distribution on the log scale with default 0

.sdlog

  Standard deviation of the distribution on the log scale with default 1

.num_sims

  The number of randomly generated simulations you want.

## Value

A tibble of randomly generated data.

## Details

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

## See also

https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm

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_normal()`, `tidy_paralogistic()`, `tidy_pareto1()`, `tidy_pareto()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Lognormal: `util_lognormal_param_estimate()`, `util_lognormal_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_lognormal()
    #> # A tibble: 50 × 7
    #>    sim_number     x     y     dx       dy      p     q
    #>    <fct>      <int> <dbl>  <dbl>    <dbl>  <dbl> <dbl>
    #>  1 1              1 1.06  -1.58  0.000877 0.522  1.06
    #>  2 1              2 3.08  -1.23  0.00545  0.870  3.08
    #>  3 1              3 1.50  -0.884 0.0241   0.658  1.50
    #>  4 1              4 0.722 -0.537 0.0767   0.373  0.722
    #>  5 1              5 0.835 -0.190 0.177    0.429  0.835
    #>  6 1              6 0.557  0.158 0.302    0.279  0.557
    #>  7 1              7 0.176  0.505 0.386    0.0412 0.176
    #>  8 1              8 1.11   0.852 0.385    0.542  1.11
    #>  9 1              9 3.27   1.20  0.312    0.882  3.27
    #> 10 1             10 0.267  1.55  0.221    0.0935 0.267
    #> # ℹ 40 more rows