R loop over linear regression - r

Question

I have looked over the forum but couldn't find what I am looking for.
I want to run a simple linear regression a couple of times. Each time using a different column as my independent variable, the dependent variable stays the same. After running it I want to be able to extract the R squared from each of the regressions. My thought process was to use a simple for loop. However, I cannot make it work.
Assume I work with the following data:
num value person1 person2 person3
0 1 229 29 81 0
1 2 203 17 75 0
2 3 244 62 0 55
and that I want to run the regression on the value using three variables: person1, person2 and person3. Note that this is a minimal working example but I hope to generalize the idea.
And so my initial attempt was to:
column <- names(df)[-2]
for(i in 3:5){
temp <- df[,c("value", column[i])]
lm.test <- lm(value ~ ., data = temp)
i + 1
}
However, when I run summary(lm.test) I only get a summary of the last regression, i.e. lm(value ~ person3) which I think makes sense but when trying to rewrite it as: lm.test[i] <- lm(value ~ ., data = temp) I get the following error:
debug at #3: temp <- df[,c("value", column[i])]
suggesting that there's something wrong with line 3?
If possible I'd like to be able to capture the summary for each regression but what I am really after is the R squared for each one of the regressions.

Answer 1

You can create formula in a loop and then run the lm. For instance, if I want to run regression on mtcars for regressing mpg on each of cyl, wt, hp, I can use the following:
vars <- c("cyl", "wt", "hp")
lm_results <- lapply(vars, function(col){
lm_formula <- as.formula(paste0("mpg ~ ", col))
lm(lm_formula, data = mtcars)
})
You can then again iterate over lm_results to get the r.squared:
lapply(lm_results, function(x) summary(x)$r.squared)

Answer 2

Here’s an approach using broom::glance() and purrr::map_dfr() to collect model summary stats into a tidy tibble:
library(broom)
library(purrr)
lm.test <- map_dfr(
set_names(names(df)[-2]),
~ glance(lm(
as.formula(paste("value ~", .x)),
data = df
)),
.id = "predictor"
)
Result:
# A tibble: 4 x 13
predictor r.squared adj.r.squared sigma statistic p.value df logLik AIC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 num 0.131 -0.739 27.4 0.150 0.765 1 -12.5 31.1
2 person1 0.836 0.672 11.9 5.10 0.265 1 -10.0 26.1
3 person2 0.542 0.0831 19.9 1.18 0.474 1 -11.6 29.2
4 person3 0.607 0.215 18.4 1.55 0.431 1 -11.3 28.7
# ... with 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
# nobs <int>
NB, you can capture model coefficients with a similar approach using broom::tidy() instead of glance().