I want to estimate the following regression models:
y <- rnorm(1:100)
x1 <- 1:100
x2 <- 1:100
x3 <- 1:100
my_data <- data.frame(cbind(y, x1, x2, x3))
m1 <- lm(y ~ x1, data = my_data)
m2 <- lm(y ~ x2, data = my_data)
m3 <- lm(y ~ x3, data = my_data)
I want to run several models like this, using the same dataset and a different independent variable in each model. How can I use a loop to run each model?
This can be easily accomplished using the apply() function:
data_to_analyse <- my_data[, -1] # leave out the y column for this analysis
out <- apply(data_to_analyse, 2, function(current_col){
lm.out <- lm(my_data$y ~ current_col)
})
This takes each column of the data.frame ("current_col") and then performs linear regression. The output of the function is a list containing the results of each regression, each entry named by the variable name.
broom::tidy the output before placing it in a list, and then dplyr::bind_rows to get one dataframe with all your results. Be sure to include .id = "name" in your bind_rows to be able to separate which output comes from which column.