View source: R/est-param-negative-binomial.R
util_negative_binomial_param_estimate | R Documentation |
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 negative binomial data.
Two different methods of shape parameters are supplied:
MLE/MME
MMUE
util_negative_binomial_param_estimate(.x, .size, .auto_gen_empirical = TRUE)
.x |
The vector of data to be passed to the function. |
.size |
The size parameter. |
.auto_gen_empirical |
This is a boolean value of TRUE/FALSE with default
set to TRUE. This will automatically create the |
This function will attempt to estimate the negative binomial size and prob parameters given some vector of values.
A tibble/list
Steven P. Sanderson II, MPH
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_gamma_param_estimate()
,
util_geometric_param_estimate()
,
util_hypergeometric_param_estimate()
,
util_logistic_param_estimate()
,
util_lognormal_param_estimate()
,
util_normal_param_estimate()
,
util_pareto_param_estimate()
,
util_poisson_param_estimate()
,
util_uniform_param_estimate()
,
util_weibull_param_estimate()
Other Binomial:
tidy_binomial()
,
tidy_negative_binomial()
,
tidy_zero_truncated_binomial()
,
tidy_zero_truncated_negative_binomial()
,
util_binomial_param_estimate()
,
util_binomial_stats_tbl()
library(dplyr)
library(ggplot2)
x <- as.integer(mtcars$mpg)
output <- util_negative_binomial_param_estimate(x, .size = 1)
output$parameter_tbl
output$combined_data_tbl %>%
tidy_combined_autoplot()
t <- rnbinom(50, 1, .1)
util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl
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