util_chisquare_param_estimate {TidyDensity} | R Documentation |
Estimate Chisquare Parameters
Description
This function will attempt to estimate the Chisquare prob parameter
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 Chisquare data.
Usage
util_chisquare_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 prob parameter of a Chisquare distribution.
The function first performs tidyeval on the input data to ensure it's a
numeric vector. It then checks if there are at least two data points, as this
is a requirement for parameter estimation.
The estimation of the chi-square distribution parameters is performed using
maximum likelihood estimation (MLE) implemented with the bbmle
package.
The negative log-likelihood function is minimized to obtain the estimates for
the degrees of freedom (doff
) and the non-centrality parameter (ncp
).
Initial values for the optimization are set based on the sample variance and
mean, but these can be adjusted if necessary.
If the estimation fails or encounters an error, the function returns NA
for both doff
and ncp
.
Finally, the function returns a tibble containing the following information:
- dist_type
The type of distribution, which is "Chisquare" in this case.
- samp_size
The sample size, i.e., the number of data points in the input vector.
- min
The minimum value of the data points.
- max
The maximum value of the data points.
- mean
The mean of the data points.
- degrees_of_freedom
The estimated degrees of freedom (
doff
) for the chi-square distribution.- ncp
The estimated non-centrality parameter (
ncp
) for the chi-square distribution.
Additionally, if the argument .auto_gen_empirical
is set to TRUE
(which is the default behavior), the function also returns a combined tibble
containing both empirical and chi-square distribution data, obtained by
calling tidy_empirical
and tidy_chisquare
, respectively.
Value
A tibble/list
Author(s)
Steven P. Sanderson II, MPH
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_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_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 Chisquare:
tidy_chisquare()
,
util_chisquare_stats_tbl()
Examples
library(dplyr)
library(ggplot2)
tc <- tidy_chisquare(.n = 500, .df = 6, .ncp = 1) |> pull(y)
output <- util_chisquare_param_estimate(tc)
output$parameter_tbl
output$combined_data_tbl |>
tidy_combined_autoplot()