Yerassyl,

I would recommend using the "Step" function in the "Faraway" library (code: library(faraway) and then step(model)) as it utilizes the AIC criterion to reduce insignificant variables. R-squared will be always its max value using all predictors and shouldn't be considered as an appropriate measure of model fit.

Hope this helps.

Dear Armin,

My friend sent me your requested code as follows:

Automated model selection and model-averaging Provide a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.

References

• http://www.jstatsoft.org/v34/i12/paper
• http://cran.r-project.org/web/packages/glmulti/index.html

Load package

library(glmulti) All-subset linear regression using lm() based on AIC

glmulti.lm.out |t|) (Intercept) 2871.99 243.67 11.79

Dear Amir, as I understood from your answer, it is designed to find the best model using prespecified number of models. Although, this technique works, but it seems to be quite hard to implement. The reason is that I need to find the best model automatically using AIC or BIC criterion without creating numbers of possible models, which can grow exponentially as the number of variables increases. In my case, I have 27 variables in my model, and it is quite challenging to construct all the possible models. If I misunderstood somewhere, please, let me know.

Stepwise and All-subsets regression are poor choices. See the references below. Best wishes, David

http://www-bcf.usc.edu/~gareth/ISL/

https://www.amazon.com/Everyone-Advanced-Analytics-Graphics-Addison-Wesley/dp/0321888030/ref=sr_1_2?ie=UTF8&qid=1510599303&sr=8-2&keywords=jared+lander&dpID=51Jib4uNx3L&preST=_SX258_BO1,204,203,200_QL70_&dpSrc=srch

Thanks very much Dr. Booth. You are helping me second time with my project.