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AutoStepwiseGLM  

Builds Stepwise GLMs via Train and Test Approach
View on CRAN: Click here


Download and install AutoStepwiseGLM package within the R console
Install from CRAN:
install.packages("AutoStepwiseGLM")

Install from Github:
library("remotes")
install_github("cran/AutoStepwiseGLM")

Install by package version:
library("remotes")
install_version("AutoStepwiseGLM", "0.2.0")



Attach the package and use:
library("AutoStepwiseGLM")
Maintained by
Aaron England
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-11-14
Latest Update: 2018-11-14
Description:
Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).
How to cite:
Aaron England (2018). AutoStepwiseGLM: Builds Stepwise GLMs via Train and Test Approach. R package version 0.2.0, https://cran.r-project.org/web/packages/AutoStepwiseGLM. Accessed 22 Dec. 2024.
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Complete documentation for AutoStepwiseGLM
Functions, R codes and Examples using the AutoStepwiseGLM R package
Some associated functions: backwd_stepwise_glm . fwd_stepwise_glm . 
Some associated R codes: BckwdStepwise.R . FwdStepwise.R .  Full AutoStepwiseGLM package functions and examples
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