Hi,
x and y are being picked up from your global environment, not from the
x and y in dataset. Here is a version that seems to work:
rm.outliers = function(dataset,var1, var2) {
dataset$varpredicted = predict(lm(as.formula(paste(var1, var2,
sep=" ~ ")), data=dataset))
dataset$varstdres = rstudent(lm(as.formula(paste(var1, var2, sep="
~ ")), data=dataset))
i = length(which(dataset$varstdres > 3 | dataset$varstdres < -3))
while(i >= 1){
removed = which(dataset$varstdres > 3 | dataset$varstdres < -3)
print(dataset[removed,])
dataset = dataset[-removed,]
dataset$varpredicted = predict(lm(as.formula(paste(var1, var2,
sep=" ~ ")), data=dataset))
dataset$varstdres = rstudent(lm(as.formula(paste(var1, var2,
sep=" ~ ")), data=dataset))
i = with(dataset,length(varstdres > 3 | varstdres < -3))
}
}
Best,
Ista
On Wed, Jan 26, 2011 at 11:36 AM, kirtau <kir...@live.com> wrote:
>
> Hi,
>
> I have a few lines of code that will remove outliers for a regression test
> based on the studentized residuals being above or below 3, -3. I have to do
> this multiple times and have attempted to create a function to lessen the
> amount of copying, pasting and replacing.
>
> I run into trouble with the function and receiving the error "Error in
> `$<-.data.frame`(`*tmp*`, "varpredicted", value = c(0.114285714285714, :
> replacement has 20 rows, data has 19
> "
>
> any help would be appreciated. a list of code is listed below.
>
> Thank you for your time!
>
> x = c(1:20)
> y = c(1,3,4,2,5,6,18,8,10,8,11,13,14,14,15,85,17,19,19,20)
> data1 = data.frame(x,y)
>
> # remove outliers for regression by studentized residuals being greater than
> 3
> data1$predicted = predict(lm(data1$y~data1$x))
> data1$stdres = rstudent(lm(data1$y~data1$x));
> i=length(which(data1$stdres>3|data1$stdres< -3))
> while(i >= 1){
> remove<-which(data1$stdres>3|data1$stdres< -3)
> print(data1[remove,])
> data1 = data1[-remove,]
> data1$predicted = predict(lm(data1$y~data1$x))
> data1$stdres = rstudent(lm(data1$y~data1$x))
> i = with(data1,length(which(stdres>3|stdres< -3)))
> }
>
> # attemp to create a function to perfom same idea as above
> rm.outliers = function(dataset,var1, var2) {
>
> dataset$varpredicted = predict(lm(var1~var2))
> dataset$varstdres = rstudent(lm(var1~var2))
> i = length(which(dataset$varstdres > 3 | dataset$varstdres < -3))
> while(i >= 1){
> removed = which(dataset$varstdres > 3 | dataset$varstdres < -3)
> print(dataset[removed,])
> dataset = dataset[-removed,]
> dataset$varpredicted = predict(lm(var1~var2))
> dataset$varstdres = rstudent(lm(var1~var2))
> i = with(dataset,length(varstdres > 3 | varstdres < -3))
> }
> }
> --
> View this message in context:
> http://r.789695.n4.nabble.com/removing-outlier-function-dataset-update-tp3238394p3238394.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> 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.
>
--
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org
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and provide commented, minimal, self-contained, reproducible code.
