Hello,
I am doing a simple linear regression analysis that includes few variables.
I am using a bootstrap analysis to obtain the variation of my variables to
replacement.
I am trying to obtain the coefficients 95% confidence interval from the
bootstrap procedure.
Here is my script for the bootstrap:
N = length (data_Pb[,1])
B = 10000
stor.r2 = rep(0,B)
stor.r2 = rep(0,B)
stor.inter = rep(0,B)
stor.Ind5 = rep(0,B)
stor.LNPRI25 = rep(0,B)
stor.NPRI10 = rep(0,B)
stor.Mine = rep(0,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.inter[i] = summary(data_Pb.boot)$coefficients[1,1]
stor.Ind5[i] = summary(data_Pb.boot)$coefficients[2,1]
stor.LNPRI25 [i] = summary(data_Pb.boot)$coefficients[3,1]
stor.NPRI10[i] = summary(data_Pb.boot)$coefficients[4,1]
stor.Mine [i] = summary(data_Pb.boot)$coefficients[5,1]
}
hist(stor.r2, xlab="R-squared",main="Distribution of R-squared - Lead
(log)")
hist(stor.inter, xlab="Intercept",main="Distribution of Intercept - Lead
(log)")
hist(stor.Ind5, xlab="Industrial 5 km",main="Distribution of Industrial 5 km
- Lead (log)")
hist(stor.LNPRI25, xlab="NPRI 25 km (log)",main="Distribution of NPRI 25 km
- Lead (log)")
hist(stor.NPRI10, xlab="NPRI 10 km",main="Distribution of NPRI 10 km - Lead
(log)")
hist(stor.Mine, xlab="Past Mines 50 km",main="Distribution of Past Mines 50
km - Lead (log)")
summary(stor.r2)
summary(stor.inter)
summary(stor.Ind5)
summary(stor.LNPRI25)
summary(stor.NPRI10)
summary(stor.Mine)
###Valid only for the last calculated model of the bootstrap analysis ???
confint(data_Pb.boot)
Can anybody show me the best way to get the 95% confidence interval of the
coefficients?
Thank you
Steeve
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