Thanks again for your swift response!!
With your last line, I get
> rowMeans(sapply(stor.confint, colMeans))
2.5 % 97.5 %
0.3256882 0.4604677
I need the values (2.5% and 97.5%) for each variable of my model. I don't
think this what I am getting.
This is what my script looks like now, after your help:
N = length (data_Pb[,1])
B = 10000
stor.r2 = rep(0,B)
stor.coeffs <- vector("list", B)
stor.confint <- vector("list", B)
for (i in 1:B){
idx = sample(1:N, replace=T)
newdata = data_Pb[idx,]
L_NPRI_25k <- log(newdata$NPRI_25k+1)
data_Pb.boot = lm(newdata$Log_Level ~
newdata$Ind_5k + newdata$MineP_50k +
newdata$NPRI_10k + L_NPRI_25k )
stor.r2[i] = summary(data_Pb.boot)$r.squared
stor.coeffs [[i]] <- coef(data_Pb.boot)
stor.confint[[i]] <- confint(data_Pb.boot)
}
hist(stor.r2, xlab="R-squared",main="Distribution of R-squared - Lead
(log)")
summary(stor.r2)
rowMeans(sapply(stor.confint, colMeans))
rowMeans(sapply(stor.coeffs, colMeans))
Thanks
Steeve
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