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Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution
Source:R/utils-aic-zt-geometric.R
util_zero_truncated_geometric_aic.Rd
This function estimates the probability parameter of a Zero-Truncated Geometric distribution from the provided data and calculates the AIC value based on the fitted distribution.
Arguments
- .x
A numeric vector containing the data to be fitted to a Zero-Truncated Geometric distribution.
Value
The AIC value calculated based on the fitted Zero-Truncated Geometric distribution to the provided data.
Details
This function calculates the Akaike Information Criterion (AIC) for a Zero-Truncated Geometric distribution fitted to the provided data.
This function fits a Zero-Truncated Geometric distribution to the provided data. It estimates the probability parameter using the method of moments and calculates the AIC value.
Optimization method: Since the parameter is directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
See also
Other Utility:
check_duplicate_rows()
,
convert_to_ts()
,
quantile_normalize()
,
tidy_mcmc_sampling()
,
util_beta_aic()
,
util_binomial_aic()
,
util_cauchy_aic()
,
util_chisq_aic()
,
util_exponential_aic()
,
util_f_aic()
,
util_gamma_aic()
,
util_generalized_beta_aic()
,
util_generalized_pareto_aic()
,
util_geometric_aic()
,
util_hypergeometric_aic()
,
util_inverse_burr_aic()
,
util_inverse_pareto_aic()
,
util_inverse_weibull_aic()
,
util_logistic_aic()
,
util_lognormal_aic()
,
util_negative_binomial_aic()
,
util_normal_aic()
,
util_paralogistic_aic()
,
util_pareto1_aic()
,
util_pareto_aic()
,
util_poisson_aic()
,
util_t_aic()
,
util_triangular_aic()
,
util_uniform_aic()
,
util_weibull_aic()
,
util_zero_truncated_binomial_aic()
,
util_zero_truncated_negative_binomial_aic()
,
util_zero_truncated_poisson_aic()
Examples
library(actuar)
#>
#> Attaching package: 'actuar'
#> The following objects are masked from 'package:stats':
#>
#> sd, var
#> The following object is masked from 'package:grDevices':
#>
#> cm
# Example: Calculate AIC for a sample dataset
set.seed(123)
x <- rztgeom(100, prob = 0.2)
util_zero_truncated_geometric_aic(x)
#> [1] 492.3338