Hi Rui,

Many thanks for your response.
I have tried to adapt your code to my problem, but there is still a mistake <text>

LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score + Quality.score,  data=Dataset)
B <- 500  # number of bootstrap samples

result <- array(dim = c(22, 3, B))
for(i in 1:B)
{s <- sample(nrow(Dataset), 22, replace = TRUE)
LinearModel.1 <- lm(GDP.per.head ~ Competitivness.score + Quality.score, data = Dataset[s, ])
result[,,i] <- predict(LinearModel.1, interval = "prediction") }

Thanks for your help. Really appreciate.

Le Jeudi 15 mai 2014 13h01, Rui Barradas <ruipbarradas@sapo.pt> a écrit :
 
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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