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March 13, 2016 18:58
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LM with a few randomly selected columns
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one_lm_attempt <- function (df, target) { | |
rnd5 <- sample(10,5) | |
predictor_variables <- names(df)[rnd5] | |
#create the formula, string to give to lm() | |
rnd_formula <- formula(paste(target, " ~ ", | |
paste(predictor_variables, collapse=" + "))) | |
m <- lm(rnd_formula, df) | |
#print (summary(m)$r.squared) | |
#print (summary(m)$adj.r.squared) | |
return(c(rnd5, summary(m)$r.squared,summary(m)$adj.r.squared )) | |
} | |
#target <- "string in your data frame that is the name of the dependent variable" | |
results <- vector() | |
for (r in 1:10){ | |
results <- c(results, one_lm_attempt(df, target)) | |
} | |
#Make the Results into a nice data frame | |
res_df <- data.frame(matrix(results, nrow=10, byrow=T)) | |
names(res_df) = c("c1","c2","c3","c4","c5","R-sq", "AdjRSq") | |
res_df |
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> res_df | |
c1 c2 c3 c4 c5 R-sq AdjRSq | |
1 6 9 7 8 10 0.7845810 0.7845650 | |
2 2 4 5 1 10 0.8984229 0.8983965 | |
3 2 7 3 9 6 0.7580219 0.7579681 | |
4 10 6 5 7 1 0.8562766 0.8562660 | |
5 2 5 1 10 7 0.8592301 0.8592118 | |
6 8 10 1 3 2 0.8771354 0.8771058 | |
7 4 5 9 3 2 0.7948897 0.7948175 | |
8 3 7 10 8 6 0.7919888 0.7919541 | |
9 10 1 5 8 6 0.8592011 0.8591880 | |
10 3 7 2 6 8 0.7951184 0.7950728 | |
> |
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