How do you resample LDA in r? - r

Question

I thought that using bootstrap would resample my LDA but I am not sure. Additionally, if bootstrap does work, I am not sure how to code a bootstrap in r
Here is my LDA code:
library('MASS')
n=nrow(iris)
train = sample(n ,size = floor(n*0.75), replace = F)
train.species =Species[train]
test.species=Species[-train]
lda.fit = lda(Species~. , data=iris, subset=train)

Answer 1

The code below uses boot() library to perform bootstrap on iris dataset using LDA to get the standard errors for the LD1 and LD2 coefficients. Also, initial part of the code shows LDA fitting without bootstrap with the same coefficients.
# Library
library(MASS)
library(boot)
# Get data
data(iris)
names(iris) <- gsub("\\.", "", names(iris)) #remove dots from column names
# Split data into train and test sets
train_index <-sample(seq(nrow(iris)),floor(dim(iris)[1]*0.75))
train <- iris[train_index,]
test <- iris[-train_index,]
test_Y <- test[, c('Species')]
test_X <- subset(test, select=-c(Species))
#### LDA without bootstrap:
# Fit LDA to train data:
lda.fit = lda(Species ~ . , data=train)
lda.fit
# Predict test_Y based on lda.fit above
lda.pred <- predict(lda.fit, test_X)
lda.class <- lda.pred$class
# Confusion matrix
table(lda.class, test_Y)
#### LDA with bootstrap:
# Fit LDA to train data: to get standard errors for coefficients
set.seed(1)
boot.fn <- function(data,index){
return(coefficients(lda(Species ~ SepalLength + SepalWidth + PetalLength + PetalWidth, data=data, subset=index)))
}
# Call boot(): This returns LD1 and LD2 for each predictor
boot(train, boot.fn, 1000)
# NOTE: Here, in Bootstrap Statistics output, t1* to t4* are LD1 coefficients and t5* to t8* are LD2 coefficients