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Estimate Zero Truncated Binomial Parameters
Source:R/est-param-zt-binom.R
util_zero_truncated_binomial_param_estimate.Rd
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 binomial data.
One method of estimating the parameters is done via:
MLE via
optim
function.
Arguments
- .x
The vector of data to be passed to the function.
- .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$combined_data_tbl
from the function output.
Details
This function will attempt to estimate the zero truncated binomial size and prob parameters given some vector of values.
See also
Other Parameter Estimation:
util_bernoulli_param_estimate()
,
util_beta_param_estimate()
,
util_binomial_param_estimate()
,
util_burr_param_estimate()
,
util_cauchy_param_estimate()
,
util_chisquare_param_estimate()
,
util_exponential_param_estimate()
,
util_f_param_estimate()
,
util_gamma_param_estimate()
,
util_generalized_beta_param_estimate()
,
util_generalized_pareto_param_estimate()
,
util_geometric_param_estimate()
,
util_hypergeometric_param_estimate()
,
util_inverse_burr_param_estimate()
,
util_inverse_pareto_param_estimate()
,
util_inverse_weibull_param_estimate()
,
util_logistic_param_estimate()
,
util_lognormal_param_estimate()
,
util_negative_binomial_param_estimate()
,
util_normal_param_estimate()
,
util_paralogistic_param_estimate()
,
util_pareto1_param_estimate()
,
util_pareto_param_estimate()
,
util_poisson_param_estimate()
,
util_t_param_estimate()
,
util_triangular_param_estimate()
,
util_uniform_param_estimate()
,
util_weibull_param_estimate()
,
util_zero_truncated_geometric_param_estimate()
,
util_zero_truncated_negative_binomial_param_estimate()
,
util_zero_truncated_poisson_param_estimate()
Other Binomial:
tidy_binomial()
,
tidy_negative_binomial()
,
tidy_zero_truncated_binomial()
,
tidy_zero_truncated_negative_binomial()
,
util_binomial_param_estimate()
,
util_binomial_stats_tbl()
,
util_negative_binomial_param_estimate()
,
util_zero_truncated_binomial_stats_tbl()
,
util_zero_truncated_negative_binomial_param_estimate()
,
util_zero_truncated_negative_binomial_stats_tbl()
Other Zero Truncated Distribution:
tidy_zero_truncated_binomial()
,
tidy_zero_truncated_geometric()
,
tidy_zero_truncated_poisson()
Examples
library(dplyr)
library(ggplot2)
x <- as.integer(mtcars$mpg)
output <- util_zero_truncated_binomial_param_estimate(x)
output$parameter_tbl
#> # A tibble: 1 × 8
#> dist_type samp_size min max mean method size prob
#> <chr> <int> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Zero-Truncated Binomial 32 10 33 19.7 MLE_Optim 33 0.597
output$combined_data_tbl |>
tidy_combined_autoplot()
set.seed(123)
t <- tidy_zero_truncated_binomial(100, 10, .1)[["y"]]
util_zero_truncated_binomial_param_estimate(t)$parameter_tbl
#> # A tibble: 1 × 8
#> dist_type samp_size min max mean method size prob
#> <chr> <int> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Zero-Truncated Binomial 100 1 4 1.53 MLE_Optim 4.00 0.500