regarding residual storing in bootstrap regression in R - r

Question

I am trying to do bootstrapping regression by re-sampling X and Y from original sample.
I followed a more manual approach (without using any package)
This is my work so far ,
set.seed(326581)
X1=rnorm(10,0,1)
Y1=rnorm(10,0,2)
data=data.frame(X1,Y1)
lst <- replicate(
100,
df.smpl <- data %>% sample_n(10, replace = T),
simplify = FALSE)
The list contained 100 samples where each sample has 2 columns (X,Y) with a sample size of 10 . These are the bootstrap samples.
to get bootstrap residuals , i separated the X and Y columns into two seperate data frames as follows,
new1=data.frame(lapply(lst, `[`, 'X1'))
new2=data.frame(lapply(lst, `[`, 'Y1))
After that i tried to store the residuals that got from each model fitted by using the following code,
res=c()
for(i in 1:100)
{
res[i]=residuals(lm(new2[,i]~new1[,i]))
}
But seems like something is wrong. Can anyone help me to figure that out ?
By the way is there any easier approach than this ?

Answer 1

You're doing this unnecessarily complicated. The whole advantage of storing objects in a list is that you can easily loop through them with e.g. lapply or sapply.
So for example, to store the residuals of a linear model fit you can do
res <- lapply(lst, function(df) residuals(lm(Y1 ~ X1, data = df)))
This fits a linear model of the form lm(Y1 ~ X1) to all data.frames in lst, and stores the residuals in a list of 100 vectors
length(res)
#[1] 100
You could also store residuals based on an lm fit to all 100 sampled data.frames in a 10x100 matrix by using sapply instead of lapply
res <- sapply(lst, function(df)
residuals(lm(Y1 ~ X1, data = df)))
dim(res)
#[1] 10 100
Update
In response to your comment you can do the following
First calculate and store residuals and residual-derived weights in every data.frame in the list.
# Add residuals and weights to lst
lst <- lapply(lst, function(df) {
df$res <- residuals(lm(Y1 ~ X1, data = df));
df$weights <- 1 / fitted(lm(abs(res) ~ X1, data = df))^2;
df;
})
Then run a weighted linear regression and return the second (slop) coefficients
# Return 2nd coeffficient of weighted regression
coeff <- lapply(lst, function(df)
coefficients(lm(Y1 ~ X1, data = df , weights = weights))[2])