util_inverse_burr_param_estimate {TidyDensity} | R Documentation |
Estimate Inverse Burr Parameters
Description
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.
Usage
util_inverse_burr_param_estimate(.x, .auto_gen_empirical = TRUE)
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 |
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.
Value
A tibble/list
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$parameter_tbl
output$combined_data_tbl |>
tidy_combined_autoplot()