I may have missed something entirely but I think ggplot2 will do this for you
pretty well automatically
library(ggplot2)
mydata <- read.csv("http://doylesdartden.com/smoothing.csv";, sep=",")
p <- ggplot(mydata , aes( X_Axis_Parameter, Y_Axis_Parameter )) +
geom_point() +
geom_smooth()
p
John Kane
Kingston ON Canada
> -----Original Message-----
> From: kydaviddo...@gmail.com
> Sent: Tue, 7 Aug 2012 21:22:41 -0500
> To: r-help@r-project.org
> Subject: [R] Confidence bands around LOESS
>
> Hi Folks,
>
> I'm looking to do Confidence bands around LOESS smoothing curve.
>
> If found the older post about using the Standard error to approximate it
> https://stat.ethz.ch/pipermail/r-help/2008-August/170011.html
>
> Also found this one
> http://www.r-bloggers.com/sab-r-metrics-basics-of-loess-regression/
>
> But they both seem to be approximations of confidence intervals and I was
> wonder if there was a way to get the CIs?
>
> Below is the code I have so far and my data is no the net.
>
> Any help would be greatly appreciated.
>
> Take Care
> David
> -----------------------------
>
> #Load your data. Is located on the web at the address below
>
> mydata <- read.csv("http://doylesdartden.com/smoothing.csv";, sep=",")
>
> mydata <- read.table("x.csv", header=TRUE, sep=",",)
>
>
>
> attach(mydata)
>
> reg1 <- lm(Y_Axis_Parameter~X_Axis_Parameter)
>
> par(cex=1)
>
> * *
>
> * *
>
> #Plots the data but makes nondetects a different color and type based on
> column D_Y_Axis_Parameter being a 0 for ND and 1 for detect.
>
> plot(X_Axis_Parameter, Y_Axis_Parameter, col=ifelse(D_Y_Axis_Parameter,
> "black", "red"),ylab = "Y_Axis_Parameter", pch=ifelse(D_Y_Axis_Parameter,
> 19, 17), cex = 0.7)
>
>
>
> plx<-predict(loess(Y_Axis_Parameter ~ X_Axis_Parameter, data=mydata),
> se=T)
>
>
>
>
>
> lines(mydata$X_Axis_Parameter,plx$fit+2*plx$s, lty=2) #rough & ready CI
>
> lines(mydata$X_Axis_Parameter,plx$fit-2*plx$s, lty=2)
>
>
>
> # Apply loess smoothing using the default span value of 0.8. You can
> change the curve by changing the span value.
>
> y.loess <- loess(y ~ x, span=0.8, data.frame(x=X_Axis_Parameter,
> y=Y_Axis_Parameter))
>
>
>
> # Compute loess smoothed values for all points along the curve
>
> y.predict <- predict(y.loess, data.frame(x=X_Axis_Parameter))
>
>
>
> # Plots the curve.
>
> lines(X_Axis_Parameter,y.predict)
>
> * *
>
> #Add Legend to graY_Axis_Parameter. You can change the size of the box
> by
> changing cex = 0.75 Large # makes it larger.
>
> legend("topleft", c("Smoothing Curve", "Detected", "NonDetect"), col =
> c(1,
> "black","red"), cex = 0.75,
>
> text.col = "black", lty = c(1 ,-1, -1), pch = c(-1, 19, 17),
>
> merge = TRUE, bg = 'gray90')
>
> * *
>
> #Add title
>
> title(main="Locally Weighted Scatterplot Smoothing Curve")
>
>
>
> # Done
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
____________________________________________________________
GET FREE SMILEYS FOR YOUR IM & EMAIL - Learn more at
http://www.inbox.com/smileys
Works with AIM®, MSN® Messenger, Yahoo!® Messenger, ICQ®, Google Talk™ and most
webmails
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
