Estimate Lognormal Parameters — util_lognormal_param_estimate • TidyDensity
Description: The function will return a list output by default, and if the parameter .auto_gen_empirical is set to TRUE then the empirical data given to the parameter .x will be run through the tidy_empirical() function and combined with the estimated lognormal data. Three different methods of shape parameters are supplied: mme, see EnvStats::elnorm() mle, see EnvStats::elnorm()

Skip to contents

# Estimate Lognormal Parameters

Source: `R/est-param-lognormal.R`
`util_lognormal_param_estimate.Rd`

The function will return a list output by default, and if the parameter `.auto_gen_empirical` is set to `TRUE` then the empirical data given to the parameter `.x` will be run through the `tidy_empirical()` function and combined with the estimated lognormal data.

Three different methods of shape parameters are supplied:

- mme, see `EnvStats::elnorm()`

- mle, see `EnvStats::elnorm()`

## Usage

    util_lognormal_param_estimate(.x, .auto_gen_empirical = TRUE)

## Arguments

.x

  The vector of data to be passed to the function.

.auto_gen_empirical

  This is a boolean value of TRUE/FALSE with default set to TRUE. This will automatically create the `tidy_empirical()` output for the `.x` parameter and use the `tidy_combine_distributions()`. The user can then plot out the data using `$combined_data_tbl` from the function output.

## Value

A tibble/list

## Details

This function will attempt to estimate the lognormal meanlog and log sd parameters given some vector of values.

## See also

Other Parameter Estimation: `util_bernoulli_param_estimate()`, `util_beta_param_estimate()`, `util_binomial_param_estimate()`, `util_burr_param_estimate()`, `util_cauchy_param_estimate()`, `util_exponential_param_estimate()`, `util_gamma_param_estimate()`, `util_geometric_param_estimate()`, `util_hypergeometric_param_estimate()`, `util_logistic_param_estimate()`, `util_negative_binomial_param_estimate()`, `util_normal_param_estimate()`, `util_pareto_param_estimate()`, `util_poisson_param_estimate()`, `util_uniform_param_estimate()`, `util_weibull_param_estimate()`

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

## Author

Steven P. Sanderson II, MPH

## Examples

    library(dplyr)
    library(ggplot2)

    x <- mtcars$mpg
    output <- util_lognormal_param_estimate(x)

    output$parameter_tbl
    #> # A tibble: 2 × 8
    #>   dist_type samp_size   min   max method        mean_log sd_log shape_ratio
    #>   <chr>         <int> <dbl> <dbl> <chr>            <dbl>  <dbl>       <dbl>
    #> 1 Lognormal        32  10.4  33.9 EnvStats_MVUE     2.96  0.298        9.93
    #> 2 Lognormal        32  10.4  33.9 EnvStats_MME      2.96  0.293       10.1

    output$combined_data_tbl %>%
      tidy_combined_autoplot()

    tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) %>% pull(y)
    util_lognormal_param_estimate(tb)$parameter_tbl
    #> # A tibble: 2 × 8
    #>   dist_type samp_size   min   max method        mean_log sd_log shape_ratio
    #>   <chr>         <int> <dbl> <dbl> <chr>            <dbl>  <dbl>       <dbl>
    #> 1 Lognormal        50  1.54  77.6 EnvStats_MVUE     2.15  0.929        2.31
    #> 2 Lognormal        50  1.54  77.6 EnvStats_MME      2.15  0.920        2.34