# Finding the best combination of variables for high R-squared values

I've been spending quite some time to figure out how I can get the best R squared value from randomization of some values in a linear regression equation. I have allele frequency data and 14 environmental gradient data. Allele frequency value is fixed, but 2~14 combinations of the 14 environmental variables are used.

My aim here is to find a combination of the environmental variables that yield high R squared value. Here is a simple linear regression equation code that returns R squared value.

> summary(lm(allele ~ compositevalues))$r.squared  "compositevalues" is a sum of standardized 14 different environmental values. I want to make 2~14 combinations of variables (with no replacement:i.e. var1+var2, var1+var3, var1+var4, var1+var2+var3, var2+var3+var4, var2+var3, var2+var4, var3+var4....etc. but not var1+var1+var2) as I mentioned above. I would appreciate it if you could instruct me on how to write a code that generate random combination of (sum of ) the variables and returns combinations of variables that are used with R squared value of >0.4. I was looking for permutation and resampling function in R, couldn't find ones that serve my purpose..... Below is a part of my data set.  1. Location allele var1 var2 var3 2. site1, 0.230271924, -0.872093023, -0.696403914, -0.398671096 3. site2, -1.061563963, 0.944767442, 1.104640692, -0.398671096 4. site3, -0.524508594, 0.339147287, -1.296752116, 0.431893688 5. site4, 0.027061785, 2.156007752, -0.096055712, 0.431893688 6. site5, 0.186726894, 0.944767442, 1.104640692, -0.398671096 7. site6, -0.118088315, -0.266472868, -0.696403914, -0.398671096 8. site7, -1.003503923, 0.339147287, -1.296752116, 0.431893688 9. site8, -1.569589312, 0.339147287, -1.296752116, 0.431893688 10. site9, -1.119624003, 0.944767442, 0.50429249, -1.22923588 11. site10, 1.362442702, -1.477713178, -0.096055712, 1.262458472 12. site11, 0.215756914, 0.339147287, -1.897100318, 1.262458472 13. site12, 0.665722223, -1.477713178, -0.096055712, 1.262458472 14. site13, 1.086657513, -1.477713178, -0.096055712, 1.262458472 15. site14, -0.001968235, 0.339147287, 1.704988894, -2.059800664 16. site15, -1.656679372, 0.339147287, 1.104640692, -2.059800664 17. site16, 0.433482064, 0.339147287, 1.704988894, -2.059800664 18. site17, -0.814808794, 1.550387597, -1.296752116, -0.398671096 19. site18, -0.713203724, 1.550387597, -0.696403914, -0.398671096  • It's an interesting combinatorial problem to maximize$R^2$, but its use for any statistical purposes, such as explanation or prediction, is highly suspect: the resulting model is very likely to be wrong. Much about this issue can be found on this site by searching with keywords related to model fitting, such as "model," "regression," "stepwise," "AIC," and many more. – whuber May 15, 2013 at 12:29 • @whuber Thanks for your comment. Yes, I was getting suspicious of results.... I'm interested in synergistic or additive effects of multiple environmental stress on allele frequency. But there are so many combinations that show significant correlations! Also, an allele frequency was correlated with multiple variables (not a composite value). Actually, some of environmental variables are correlated to each other. So I could choose one of them or put them together to treat it as a composite factor. But still this doesn't solve the issue I'm having here.... Please help! May 16, 2013 at 6:09 • A good place to start would be with a perusal of any interesting hits you can find in a search on model selection: focus on the highest-voted ones first, because there are over a thousand posts containing these keywords! – whuber May 16, 2013 at 14:00 • Young Can please you share your code? – user60361 Nov 10, 2014 at 10:29 ## 1 Answer In your case, it might be feasible to try out all combinations (there are 16383 combinations) of sums. I wrote a quick and dirty implementation of that. With 14 variables it takes less than a minute to try out all combinations. If you want a random combination, you can modify the code to meet your needs. my.vars <- matrix(NA, ncol=14, nrow=) # a matrix with your 14 different environmental variables colnames(my.vars) <- paste("var", 1:14, sep="") # add row names "var1" - "var14" my.grad.data <- 1:14 sum.vars <- vector() r.2 <- vector() comb.mat <- matrix(numeric(0), nrow=14, ncol=0) # initialise the matrix containing all combinations for ( i in 1:14 ) { # generate and store all possible combination of sums of the 14 variables t.mat <- combn(my.grad.data, m=i) comb.mat <- cbind(comb.mat, rbind(t.mat, matrix(NA, ncol=dim(t.mat)[2] , nrow=14-i))) } for ( j in 1:dim(comb.mat)[2] ) { # calculate and store the R2 for all combinations sum.vec <- rowSums(my.vars[, comb.mat[, j]], na.rm=TRUE) sum.vars[j] <- paste( colnames(my.vars[, comb.mat[, j]])[!is.na(colnames(my.vars[, comb.mat[, j]]))], collapse="+") r.2[j] <- summary(lm(allele ~ sum.vec))$r.squared
}

result.frame <- data.frame(combination=sum.vars, r2=r.2)

result.frame.sorted <- result.frame[order(r.2, decreasing=TRUE), ]

head(result.frame.sorted, n=10) # the 10 "best" combinations