[R] Confidence bands around LOESS

[John Kane]

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: kydaviddoyle at gmail.com
> Sent: Tue, 7 Aug 2012 21:22:41 -0500
> To: r-help at 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]]
> 
> ______________________________________________
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> PLEASE do read the posting guide
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