[R] anomalies with the loess() function

[Peter Ehlers]

On 2010-10-26 11:48, Jonathan P Daily wrote:
> ?loess
>
> use this instead:
>
> fit<- loess(b~a)
> lines(a, predict(fit))

I don't think that will work when there are incomplete cases,
in which case 'a' and predict(fit) may not correspond.

I think that it's always best to define a set of predictor
values and use predict() to get the corresponding fits and
plot according to taste:

  fm <- loess(b ~ a)
  aa <- seq(0, 1000, length=101)
  bb <- predict(fm, aa)
  lines(aa, bb, col="blue", lwd=2)

@Federico: see further comments below.

> --------------------------------------
> Jonathan P. Daily
> Technician - USGS Leetown Science Center
> 11649 Leetown Road
> Kearneysville WV, 25430
> (304) 724-4480
> "Is the room still a room when its empty? Does the room,
>   the thing itself have purpose? Or do we, what's the word... imbue it."
>       - Jubal Early, Firefly
>
>
>
> From:
> Federico Bonofiglio<bonoricus at gmail.com>
> To:
> r-help at r-project.org
> Date:
> 10/26/2010 02:38 PM
> Subject:
> [R] anomalies with the loess() function
> Sent by:
> r-help-bounces at r-project.org
>
>
>
> Hello Masters,
>
> I run the loess() function to obtain local weighted regressions, given
> lowess() can't handle NAs, but I don't
> improve significantly my situation......, actually loess() performance
> leave
> me much puzzled....
>
> I attach my easy experiment below
>
> #------SCRIPT----------------------------------------------
>
> #I explore the functionalities of lowess()&  loess()
> #because I have encountered problems in execute local weighted regressions
> #with lowess() (in presence of NAs)&  with loess() (always!!!)
>
>
> #I generate 2 fictious vectors
>
> a<-sample(c(sample(1:1000,100),rep(NA,50)))
>
> b<-sample(c(sample(1:1000,100),rep(NA,50)))
>
> #lm() has no problems..can handle the missing values
> plot(a,b)
> abline(lm(b~a),col="red",lwd=2)
>
> #loess return a plain mess like it would go dizzed with ordering or
> something.

Yes, the 'mess' is due to the unordered nature of your data.
lines() will plot in the order in which the points occur in
your data. You could order before calling loess:

  ord <- order(a)
  a1 <- a[ord]
  b1 <- b[ord]
  fm <- loess(b1 ~ a1)

> #Off course lowess() turns useless in presence of NAs, I don't even try
> it.
>
> lines(loess(b~a))
>
> #I get rid off NAs and compare lowess()&  loess() performance, expecting
> to
> #obtain the same result as both functions implement local weighted
> regressions
>
> a<-na.omit(a)
> b<-na.omit(b)

This is a bad idea. The values of 'a' and 'b' will no longer
be paired. Another reason to prefer dataframes.

>
> #check out the evidence.....something's wrong with loess()???

There's nothing wrong with loess; it just needs more than a
single intercept and slope to plot its predictions.

   -Peter Ehlers

>
> par(mfrow=c(1,2))
> plot(a,b)
> lines(lowess(a,b),col="red")#if NAs are excluded lowess() runs regularly
> plot(a,b)
> lines(loess(b~a),col="red")#.....but loess() keeps messing all
> over...!!???
>