[R] strange behavior of loess() & predict()

[Leo Gürtler]

Gavin Simpson wrote:

Dear list,

I am very sorry for being inaccurate in my question. But re-reading the 
predict.loess help site does not provide a solution. As long as predict 
is used on a new dataset based on this dataset, the strange values 
remain and can be reproduced.
Adding a new element to both vectors (at the beginning, e.g. "1" for 
each vector) results in plausible values - but not in every case.
Even switching x and y is sufficient (i.e. x as predictor and y as 
dependent variable). So my question is:

Is it normal - or under which conditions does it take place - that 
predict.loess predicts values that are almost 20000/max(y) ~ 5000 times 
higher than expected?

best,

leo gÃ¼rtler

>On Tue, 2005-12-06 at 18:09 +0100, Leo GÃ¼rtler wrote:
>  
>
>>Dear altogether,
>>    
>>
><snip>
>  
>
>># here is the difference!!
>>predict(mod, data.frame(x=X), se=TRUE)
>>predict(mod, x=X, se=TRUE)
>>
>>
>><--- end of snip --->
>>
>>I assume this has some reason but I do not understand this reason.
>>Merci,
>>    
>>
>
>Not sure if this is the reason, but there is no argument x in
>predict.loess, and:
>
>a <- predict(mod, se = TRUE)
>
>gives you the same results as:
>
>b <- predict(mod, x=X, se=TRUE)
>
>so the x argument appears to be being passed on/in the ... arguments and
>ignored? As such, you have no newdata, so mod$x is used.
>
>Now, when you do:
>
>c <- predict(mod, data.frame(x=X), se=TRUE)
>
>You have used an un-named argument in position 2. R takes this to be
>what you want to use for newdata and so works with this data rather than
>the one in mod$x as in the first case:
>
># now named second argument - gets ignored as in a and b
>d <- predict(mod, x = data.frame(x=X), se=TRUE)
>
>all.equal(a, b) # TRUE
>all.equal(a, c) # FALSE
>all.equal(a, d) # TRUE
>
># this time we assign X to x by using (), the result is used as newdata
>e <-  predict(mod, (x=X), se=TRUE)
>
>all.equal(c, e) # TRUE
>
>If in doubt, name your arguments and check the help! ?predict.loess
>would have quickly shown you where the problem lay.
>
>HTH
>
>G
>
>  
>
>>best regards
>>
>>leo gÃ¼rtler
>>
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>>    
>>


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