Hi, Ive been trying to write a program for bootstrapping residuals in R but without much success.
A lecturer wants to predict the performance of students in an end-of-year physics exam, y. The lecturer has the students results from a mid-term physics exam, x, and a mid-term biology exam, z.
The lecturer proposes the following linear model for the end-of-year exam result
yi = α + βxi + γzi + qi, where q is the error.
Y is a matrix of the data and we have y=first column of the data and X=second 2 columns(the x & z data)
Now I need to write a program for obtaining bootstrap estimates, i have:
x=scan(data)
Y=matrix(x,ncol=3,byrow=T)
y=Y[,1]
X=Y[,2:3]
ls=lsfit(X,y)
beta=ls$coef
yest=beta[1]+beta[2]*X[,1]+beta[3]*X[,2]
res=y-yest
boot=function(X,res,beta,b)
{
n=24
output=matrix(0,ncol=2,nrow=b)
for(i in 1:b)
{
error=sample(res,n,replace=T)
ystar=beta[1]+beta[2]*X[,1]+beta[3]*X[,2]+error
ls=lsfit(X,ystar)
output[i,]=ls$coef
}
output
}
I think the first 8 lines are right but my function might be wrong?
Any help?