R: repeat linear regression for all variables and save results in a new data frame

Question

I have a data frame named “dat” with 10 numeric variables (var1, var2,var3,var4 , var5,…var 10), each with several observations…

dat

   var1 var2 var3 var4 var         


        

Answer 1

There a several ways to do what you want in R. I suggest sapply which is a simple way to apply a function other a list of variables. Here is an example to get the coefficients of each linear regression between var1 and all other variables.

# define a function to get coefficients from linear regression
do_lm <- function(var){ # var is the name of the column
  res <- lm(as.formula(paste0("var1~",var)), data = dat) # compute linear regression
  coefs <- c(intercept = res$coefficient[2], slope = res$coefficient[1]) # get coefficients
  return(coefs)
}

t(
  sapply(colnames(dat)[2:10], do_lm)
 )
# t transposes the result 
# sapply : applies on "var2" ... "var10" the function do_lm

It returns :

      intercept.var2 slope.(Intercept)
var2       0.5251232         6.4600985
var3       0.8630573        -1.4968153
var4       0.7660377         0.6490566
var5      -0.5047619        14.8158730
var6              NA        10.7777778
var7              NA        10.7777778
var8              NA        10.7777778
var9              NA        10.7777778
var10             NA        10.7777778

You can adapt the function do_lm in sapply to compute other things, like correlations ...