I have 3 dataframes (df1, df2, df3) with the same variable names, and I would like to perform essentially the same regressions on all 3 dataframes. My regressions currently look like this:
m1 <- lm(y ~ x1 + x2, df1)
m2 <- lm(y~ x1 + x2, df2)
m3<- lm(y~ x1 + x2, df3)
Is there a way I can use for-loops in order to perform these regressions by just swapping out dataframe used?
Thank you
Using update.
(fit <- lm(Y ~ X1 + X2 + X3, df1))
# Call:
# lm(formula = Y ~ X1 + X2 + X3, data = df1)
#
# Coefficients:
# (Intercept) X1 X2 X3
# 0.9416 -0.2400 0.6481 0.9357
update(fit, data=df2)
# Call:
# lm(formula = Y ~ X1 + X2 + X3, data = df2)
#
# Coefficients:
# (Intercept) X1 X2 X3
# 0.6948 0.3199 0.6255 0.9588
Or lapply
lapply(mget(ls(pattern='^df\\d$')), lm, formula=Y ~ X1 + X2 + X3)
# $df1
#
# Call:
# FUN(formula = ..1, data = X[[i]])
#
# Coefficients:
# (Intercept) X1 X2 X3
# 0.9416 -0.2400 0.6481 0.9357
#
#
# $df2
#
# Call:
# FUN(formula = ..1, data = X[[i]])
#
# Coefficients:
# (Intercept) X1 X2 X3
# 0.6948 0.3199 0.6255 0.9588
#
#
# $df3
#
# Call:
# FUN(formula = ..1, data = X[[i]])
#
# Coefficients:
# (Intercept) X1 X2 X3
# 0.5720 0.6106 -0.1576 1.1391
Data:
set.seed(42)
f <- \() transform(data.frame(X1=rnorm(10), X2=rnorm(10), X3=rnorm(10)),
Y=1 + .2*X1 + .4*X2 + .8*X3 + rnorm(10))
set.seed(42); df1 <- f(); df2 <- f()
or add the dataframes to a list and map the lm function over the list.
library(tidyverse)
df1 <- tibble(x = 1:20, y = 3*x + rnorm(20, sd = 5))
df2 <- tibble(x = 1:20, y = 3*x + rnorm(20, sd = 5))
df3 <- tibble(x = 1:20, y = 3*x + rnorm(20, sd = 5))
df_list <- list(df1, df2, df3)
m <- map(df_list, ~lm(y ~ x, data = .))