loess problems
The problem is that 90% of your data sit on the boundary. Loess is a nearest neighbor smoother (using (100 x span) % of the data to estimate at each point). If you call loess() directly with span=2/3 (the default in scatter.smooth), or something smaller than about 0.91, you'll see that it has trouble. Strangely, if you set span=.8, scatter.smooth() will also complain, but not at the default span... (Re-generating the data yet again does trigger the warnings, so seems like it does catches things some of the time.) For your second example, I think loess becomes undefined when the span is set too small (and 1/n is surely too small): You are asking the algorithm to take the nearest 1/n of the data to do the smooth. You would think that should just mean _the_ nearest data point, but the problem is:
n <- 100 1 / n < 1
[1] TRUE so you're asking the algoithm to take fewer than 1 data point to estimate at each point. The warnings you see for that example is pointing you in the right direction. Andy
From: Jean Eid I have a problem either understanding what loess is doing or that loess has a problem itself. As the x-axis variables become more concentrated on a particular point,the estimated loess tends to zero????. the examples below show what i am talking about, why is that? my intution tells me that it should tend to the mean of the variable which is been smoothed. Here's a worked up example x <- c(seq(0,100), rep(100,1000)) y <- rnorm(length(x), mean=10, sd=2) scatter.smooth(x,y) Although it does give warnings, I don't understand why it is giving the estimate as zero. another example would be x <- seq(0,100) y <- rnorm(length(x), mean=50, sd=2) scatter.smooth(x,y, span=1/length(x)) shoudn't this give just the points at which the smoothing algorithm is applied? thank you
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