This function will attempt to estimate the gamma shape and scale
parameters given some vector of values. 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 gamma data.
Arguments
- .x
The vector of data to be passed to the function. Must be numeric.
- .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 thetidy_combine_distributions()
. The user can then plot out the data using$combined_data_tbl
from the function output.
See also
Other Parameter Estimation:
util_bernoulli_param_estimate()
,
util_beta_param_estimate()
,
util_binomial_param_estimate()
,
util_burr_param_estimate()
,
util_cauchy_param_estimate()
,
util_exponential_param_estimate()
,
util_geometric_param_estimate()
,
util_hypergeometric_param_estimate()
,
util_logistic_param_estimate()
,
util_lognormal_param_estimate()
,
util_negative_binomial_param_estimate()
,
util_normal_param_estimate()
,
util_pareto_param_estimate()
,
util_poisson_param_estimate()
,
util_triangular_param_estimate()
,
util_uniform_param_estimate()
,
util_weibull_param_estimate()
Other Gamma:
tidy_gamma()
,
tidy_inverse_gamma()
,
util_gamma_stats_tbl()
Examples
library(dplyr)
library(ggplot2)
tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y)
output <- util_gamma_param_estimate(tg)
output$parameter_tbl
#> # A tibble: 3 × 10
#> dist_type samp_size min max mean variance method shape scale
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Gamma 50 0.00391 0.807 0.246 0.225 NIST_MME 1.20 0.205
#> 2 Gamma 50 0.00391 0.807 0.246 0.225 EnvStats_MMUE 1.18 0.205
#> 3 Gamma 50 0.00391 0.807 0.246 0.225 EnvStats_BCMLE 1.14 0.205
#> # ℹ 1 more variable: shape_ratio <dbl>
output$combined_data_tbl |>
tidy_combined_autoplot()