An Overview of the New AIC Functions in the TidyDensity Package
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Introduction
The latest update the the TidyDensity package introduces several new functions that make it easier to work with data in R. In this article, we’ll take a look at the new AIC functions and how they work.
New Functions
The set of functions that we will go over are the util_dist_aic() functions, where dist is the distribution in question, for example util_negative_binomial_aic(). These functions calculate the Akaike Information Criterion (AIC) for a given distribution and data. The AIC is a measure of the relative quality of a statistical model for a given set of data. The lower the AIC value, the better the model fits the data. Here is a bit about the functions.
Usage
util_negative_binomial_aic()
Arguments
.x: A numeric vector of data values.
Value
A numeric value representing the AIC for the given data and distribution.
Details
This function calculates the Akaike Information Criterion (AIC) for a distribution fitted to the provided data.
This function fits a distribution to the provided data. It estimates the parameters of the distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the param estimate family of functions in order to estimate the starting point of the parameters. For example util_negative_binomial_param_estimate().
Optimization method: Since the parameters are 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.
Examples
library(TidyDensity) set.seed(123) # Generate some data x <- rnorm(100) # Calculate the AIC for a negative binomial distribution cat( " AIC of rnorm() using TidyDensity: ", util_normal_aic(x), "\n", "AIC of rnorm() using fitdistrplus: ", fitdistrplus::fitdist(x, "norm")$aic )
AIC of rnorm() using TidyDensity: 268.5385 AIC of rnorm() using fitdistrplus: 268.5385
New AIC Functions
Here is a listing of all of the new AIC functions:
util_negative_binomial_aic()util_zero_truncated_negative_binomial_aic()util_zero_truncated_poisson_aic()util_f_aic()util_zero_truncated_geometric_aic()util_t_aic()util_pareto1_aic()util_paralogistic_aic()util_inverse_weibull_aic()util_pareto_aic()util_inverse_burr_aic()util_generalized_pareto_aic()util_generalized_beta_aic()util_zero_truncated_binomial_aic()
Conclusion
Thanks for reading. I hope you find these new functions useful in your work. If you have any questions or feedback, please feel free to reach out. I worked hard to ensure where I could that results would come back identical to what would be calculated from the amazing fitdistrplus package.
Happy Coding!
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