##### Compute and plot confidence intervals on regression:
linesCI <-function(x, level=0.95, col=NULL, lty=NULL, ...){
if(is.null(col)){col="black"}
if(is.null(lty)){lty=2}
p <- 1 - {{1-level}/2}
xm <- sapply(x$model[2], mean)
n <- nrow(x$model[2])
ssx <- sum(x$model[2]^2)-sum(x$model[2])^2/n
s.t <- qt(p,(n-2))
xv <- seq(min(x$model[2]),max(x$model[2]),(max(x$model[2])-min(x$model[2]))/100)
yv <- coef(x)[1]+coef(x)[2]*xv
se <- sqrt(summary(x)$sigma^2*(1/n+(xv-xm)^2/ssx))
ci <- s.t*se
uyv <- yv+ci
lyv <- yv-ci
lines(xv, uyv, lty=lty, col=col, ...)
lines(xv, lyv, lty=lty, col=col, ...)
}
# ##### Compute and plot confidence intervals on regression:
# linesCI <-function(x,col=NULL){
# if(is.null(col)){col="black"}
# xm<-sapply(x$model[2], mean)
# n<-length(x$model[[2]])
# ssx<-sum(x$model[2]^2)-sum(x$model[2])^2/n
# s.t<-qt(0.975,(n-2))
# xv<-seq(min(x$model[2]),max(x$model[2]),(max(x$model[2])-min(x$model[2]))/100)
# yv<-coef(x)[1]+coef(x)[2]*xv
# se<-sqrt(summary(x)$sigma^2*(1/n+(xv-xm)^2/ssx))
# ci<-s.t*se
# uyv<-yv+ci
# lyv<-yv-ci
# lines(xv,uyv,lty=2, col=col)
# lines(xv,lyv,lty=2, col=col)
# }
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