Output Regression statistics for each variable one at a time in R

Question

I have a data frame that looks like this. names and number of columns will NOT be consistent (sometimes \'C\' will not be present, other times \"D\', \'E\', \'F\' may be pre

Answer 1

Here is a solution using *apply:

Y <- c(4, 4, 3, 4, 3, 2, 3, 2, 2, 3, 4, 4, 3, 4, 8, 6, 5, 4, 3, 6)
A <- c(1, 2, 1, 2, 3, 2, 1, 1, 1, 2, 1, 4, 3, 1, 2, 2, 1, 2, 4, 8)
B <- c(5, 6, 6, 5, 3, 7, 2, 1, 1, 2, 7, 4, 7, 8, 5, 7, 6, 6, 4, 7)
C <- c(9, 1, 2, 2, 1, 4, 5, 6, 7, 8, 89, 9, 7, 6, 5, 6, 8, 9 , 67, 6)
YABC <- data.frame(Y, A, B, C)

names <- colnames(YABC[-1])

formulae <- sapply(names,function(x)as.formula(paste('Y~',x)))

lapply(formulae, function(x) lm(x, data = YABC))

Of course you can also call summary:

lapply(formulae, function(x) summary(lm(x, data = YABC)))

If you want to extract variables from a specific model do as follows:

results <- lapply(formulae, function(x) lm(x, data = YABC))
results$A$coefficients

gives the coefficients from the model using A as explanatory var