library(lqs)
g <- ltsreg(involact ~ race+fire+theft+age+log(income),chicago)
help(ltsreg)
g
g <- ltsreg(involact ~ race+fire+theft+age+log(income),chicago,nsamp="best")
g
g <- ltsreg(involact ~ race+fire+theft+age+log(income),chicago,nsamp="best")
g <- ltsreg(involact ~ race+fire+theft+age+log(income),chicago,nsamp="best")
g
g <- ltsreg(involact ~ race+fire+theft+age+log(income),chicago,nsamp="exact")
g$res
sample(c("A","B","C"),10,rep=T)
sample(c("A","B","C"),10,rep=T)
sample(c("A","B","C"),10,rep=T)
x <- model.matrix(g)[,-1]
x <- model.matrix(~race+fire+theft+age+log(income),chicago)
x <- x[,-1]
newy <- g$fit+g$res[sample(47,rep=T)]
newy <- g$fit+sample(g$res,47,rep=T)
nbeta <- ltsreg(x,newy,nsamp="best")
nbeta
bcoef <- matrix(0,100,6)
for(i in 1:100){
newy <- g$fit+sample(g$res,47,rep=T)
nbeta <- ltsreg(x,newy,nsamp="best")
bcoef[i,] <- nbeta$coef
}
bcoef <- matrix(0,1000,6)
for(i in 1:1000){
newy <- g$fit+sample(g$res,47,rep=T)
nbeta <- ltsreg(x,newy,nsamp="best")
bcoef[i,] <- nbeta$coef}
bcoef
hist(bcoef[,2])
plot(density(bcoef[,2]))
abline(v=0)
length(bcoef[bcoef[,2]<0,2])
quantile(bcoef[,2],c(0.025,0.975))