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.
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 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.
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)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
output$parameter_tbl
#> # A tibble: 1 × 7
#> dist_type samp_size min max mean dof ncp
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Chisquare 500 0.242 23.1 6.92 6.08 0.844
output$combined_data_tbl |>
tidy_combined_autoplot()
