Hello,
baconbeach wrote
>
> 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
>
I think you're complicating a bit, you could save r2 in a vector and the
coefficients in a list (see (*) below),
like the one that follows:
After creating your stor vars, and before the bootstarp loop, put this
stor.confint <- vector("list", B)
Then, inside the loop, at it's end,
stor.confint[[i]] <- confint(data_Pb.boot)
This creates a list of matrices.
(*) The same for the coefficients, create a list first then in the loop use
function coef()
stor.coeffs <- vector("list", B)
stor.coeffs <- coef(data_Pb.boot)
Hope this helps,
Rui Barradas
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