Automatic selection of explanatory variable of multiple regression analysis by genetic algorithm.
It is generally difficult to select explanatory variables in multiple regression analysis. This package automatically selects explanatory variables of multiple regression analysis with genetic algorithm and it will be useful for creating your statistical model.
You can install from R console.
If devtools is not installed on your PC, install devtools with Internet connection.
install.packages("devtools")
Install from GitHub using devtools.
library(devtools)
install_github("ToshihiroIguchi/gaglm")
Load the gaglm package and attach it.
library(gaglm)
Installation may fail if running under proxy environment.
In that case, you may be able to install using the httr package.
We explain the usage of gaglm function specifically using attitude.
Like the glm function, write an expression in formula form and specify data.frame for data.
The objective variable is rating and the explanatory variable is all other items.
If method is not specified, method = "AIC".
result <- gaglm(rating ~ complaints + privileges + learning + raises + critical + advance, data = attitude)
By using the plot function, it shows the selection of variables by genetic algorithm, the transition of information quantity criteria, and the result of regression diagnosis.
plot(result)
An optimal explanatory variable by genetic algorithm was found in a short time. There is no problem with the result of regression diagnosis.
In addition, by using the summary function, we can confirm the result with the smallest information criterion.
summary(result)
If variable selection does not work, it may be solved by changing the value of zeroToOneRatio of gaglm function.
If method = "CV", search for a model that minimizes RMSE of cross validation.
However, only family = "gaussian" can be selected.
result <- gaglm(rating ~ complaints + privileges + learning + raises + critical + advance, data = attitude, method = "CV", popSize = 30)
The result is displayed by the plot function.
If method = "CV", the result of cross varidation is also displayed.
plot(result)
Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.
C.B. Lucasius and G. Kateman (1993). Understanding and using genetic algorithms - Part 1. Concepts, properties and context. Chemometrics and Intelligent Laboratory Systems 19:1-33.
C.B. Lucasius and G. Kateman (1994). Understanding and using genetic algorithms - Part 2. Representation, configuration and hybridization. Chemometrics and Intelligent Laboratory Systems 25:99-145.
Chatterjee, S. and Price, B. (1977) Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)
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Copyright (c) 2017 Toshihiro Iguchi
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Toshihiro Iguchi