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 inverse Pareto data.
util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)A tibble/list
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 $combined_data_tbl from the function output.
Steven P. Sanderson II, MPH
This function will attempt to estimate the inverse Pareto shape and scale parameters given some vector of values.
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_burr_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Inverse Pareto:
util_inverse_pareto_stats_tbl()
library(dplyr)
library(ggplot2)
set.seed(123)
x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]]
output <- util_inverse_pareto_param_estimate(x)
output$parameter_tbl
output$combined_data_tbl %>%
tidy_combined_autoplot()
Run the code above in your browser using DataLab