This function will attempt to estimate the inverse Burr shape1, shape2, and rate parameters
given some vector of values .x. 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 Burr data.
Arguments
- .x
The vector of data to be passed to the function. Must be non-negative integers.
- .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.xparameter and use thetidy_combine_distributions(). The user can then plot out the data using$combined_data_tblfrom the function output.
Details
This function will see if the given vector .x is a numeric vector.
It will attempt to estimate the shape1, shape2, and rate parameters of an inverse
Burr distribution.
See also
Other Parameter Estimation:
util_bernoulli_param_estimate(),
util_beta_param_estimate(),
util_binomial_param_estimate(),
util_burr_param_estimate(),
util_cauchy_param_estimate(),
util_chisquare_param_estimate(),
util_exponential_param_estimate(),
util_f_param_estimate(),
util_gamma_param_estimate(),
util_generalized_beta_param_estimate(),
util_generalized_pareto_param_estimate(),
util_geometric_param_estimate(),
util_hypergeometric_param_estimate(),
util_inverse_pareto_param_estimate(),
util_inverse_weibull_param_estimate(),
util_logistic_param_estimate(),
util_lognormal_param_estimate(),
util_negative_binomial_param_estimate(),
util_normal_param_estimate(),
util_paralogistic_param_estimate(),
util_pareto1_param_estimate(),
util_pareto_param_estimate(),
util_poisson_param_estimate(),
util_t_param_estimate(),
util_triangular_param_estimate(),
util_uniform_param_estimate(),
util_weibull_param_estimate(),
util_zero_truncated_binomial_param_estimate(),
util_zero_truncated_geometric_param_estimate(),
util_zero_truncated_negative_binomial_param_estimate(),
util_zero_truncated_poisson_param_estimate()
Other Inverse Burr:
util_inverse_burr_stats_tbl()
Examples
library(dplyr)
library(ggplot2)
set.seed(123)
tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y)
output <- util_inverse_burr_param_estimate(tb)
output$parameter_tbl
#> # A tibble: 1 × 9
#> dist_type samp_size min max mean shape1 shape2 rate scale
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Inverse Burr 50 0.253 21.0 4.38 0.692 2.32 0.245 4.08
output$combined_data_tbl |>
tidy_combined_autoplot()
