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 pareto data.
Two different methods of shape parameters are supplied:
LSE
MLE
util_pareto_estimate(.x, .auto_gen_empirical = TRUE)
The vector of data to be passed to the function.
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 $combined_data_tbl
from the function output.
A tibble/list
This function will attempt to estimate the pareto shape and scale parameters given some vector of values.
Other Parameter Estimation:
util_beta_param_estimate()
,
util_binomial_param_estimate()
,
util_exponential_param_estimate()
,
util_gamma_param_estimate()
,
util_geometric_param_estimate()
,
util_hypergeometric_param_estimate()
,
util_logistic_param_estimate()
,
util_lognormal_param_estimate()
,
util_negative_binomial_param_estimate()
,
util_normal_param_estimate()
Other Pareto:
tidy_generalized_pareto()
,
tidy_inverse_pareto()
,
tidy_pareto1()
,
tidy_pareto()
library(dplyr)
library(ggplot2)
x <- mtcars$mpg
output <- util_pareto_estimate(x)
output$parameter_tbl
#> # A tibble: 2 x 8
#> dist_type samp_size min max method shape scale shape_ratio
#> <chr> <int> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Pareto 32 10.4 33.9 LSE 13.7 2.86 4.79
#> 2 Pareto 32 10.4 33.9 MLE 10.4 1.62 6.40
output$combined_data_tbl %>%
ggplot(aes(x = dx, y = dy, group = dist_type, color = dist_type)) +
geom_line() +
theme_minimal() +
theme(legend.position = "bottom")
t <- tidy_pareto(50, 1, 1) %>% pull(y)
util_pareto_estimate(t)$parameter_tbl
#> # A tibble: 2 x 8
#> dist_type samp_size min max method shape scale shape_ratio
#> <chr> <int> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Pareto 50 0.0105 184. LSE 0.110 0.467 0.236
#> 2 Pareto 50 0.0105 184. MLE 0.0105 0.224 0.0468