Hello,
Try to follow the example below and see if you can adapt it to your
needs. Since you don't provide us with a dataset example, I start by
making up some.
# make up some data
n <- 22
set.seed(8873)
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n))
dat$y <- x1 + x2 + rnorm(n)
B <- 100 # number of bootstrap samples
result <- array(dim = c(n, 3, B), dimnames = list(NULL, c("fit", "upr",
"lwr"), NULL))
for(i in 1:B){
s <- sample(nrow(dat), n, replace = TRUE)
lm.tmp <- lm(y ~ x1 + x2, data = dat[s, ])
result[,,i] <- predict(lm.tmp, interval = "prediction")
}
Then you can do whatever you want with 'result', including computing the
min and max values.
Hope this helps,
Rui Barradas
Em 15-05-2014 10:37, varin sacha escreveu:
Dear experts,
I have done a multiple linear regression on a small sample size (n=22).
I have computed the prediction intervals (not the confidence intervals).
Now I am trying to bootstrap the prediction intervals.
I didn't find any package doing that.
So I decide to create my own R function, but it doesn't work !
Here are my R codes :
LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score +Quality.score,
data=Dataset)
summary(LinearModel.1)
predict(LinearModel.1, interval = "prediction")
HERE IS MY R FUNCTION WHERE I HAVE TRIED TO BOOTSTRAP THE PREDICTION INTERVALS
pred.min<-rep(nrow(Dataset), na.rm=F)
pred.max<-rep(nrow(Dataset), na.rm=F)
for(i in 1:n)
{s<-sample(1:nrow(Dataset),size=22)
reg<-lm(GDP.per.head ~ Competitivness.score + Quality.score,data=Dataset[s])
pred.min<-pmin(reg,pred.min)
pred.max<-pmax(reg,pred.max)}
Thanks for your precious help.
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