fitting linear regression models with different predictors using loops

Question

I want to fit regression models using a single predictor variable at a time. In total I have 7 predictors and 1 response variable. I want to write a chunk of code that picks a predictor variable from data frame and fits a model. I would further want to extract regression coefficient( not the intercept) and the sign of it and store them in 2 vectors. Here's my code-

for (x in (1:7))
{
  
fit <- lm(distance ~ FAA_unique_with_duration_filtered[x] , data=FAA_unique_with_duration_filtered)
coeff_values<-summary(fit)$coefficients[,1]
coeff_value<-coeff_values[2]
append(coeff_value_vector,coeff_value , after = length(coeff_value_vector))
append(RCs_sign_vector ,sign(coeff_values[2]) , after = length(RCs_sign_vector))
}

Over here x in will use the first column, then the 2nd and so on. However, I am getting the following error.

Error in model.frame.default(formula = distance ~ FAA_unique_with_duration_filtered[x], : invalid type (list) for variable 'FAA_unique_with_duration_filtered[x]'

Is there a way to do this using loops?

Answer 1

You don't really need loops for this.

Suppose we want to regress y1, the 5th column of the built-in anscombe dataset, separately on each of the first 4 columns.

Then:

a <- anscombe
reg <- function(i) coef(lm(y1 ~., a[c(5, i)]))[[2]] # use lm
coefs <- sapply(1:4, reg)
signs <- sign(coefs)

# or

a <- anscombe
reg <- function(i) cov(a$y1, a[[i]]) / var(a[[i]]) # use formula for slope
coefs <- sapply(1:4, reg)
signs <- sign(coefs)

Alternately the following where reg is either of the reg definitions above.

a <- anscombe
coefs <- numeric(4)
for(i in 1:4) coefs[i] <- reg(i)
signs <- sign(coefs)