On Jan 2, 2015, at 4:58 AM, Methekar, Pushpa (GE Transportation, Non-GE) wrote:
> Hi ,
> I am working on a function .
>
> rm.outliers = function(dataset,model){
> dataset$predicted = predict(model)
> dataset$stdres = rstudent(model)
> m = 1
> for(i in 1:length(dataset$stdres)){
> dataset$outlier_counter[i] = if(dataset$stdres[i] >= 3 |
> dataset$stdres[i] <= -3) {m}
> else{0}
> }
> j = length(which(dataset$outlier_counter >= 1))
> while(j>=1){
> print(dataset[which(dataset$outlier_counter >= 1),])
> dataset = dataset[which(dataset$outlier_counter == 0),]
> dataset$predicted = predict(model)
> dataset$stdres = rstudent(model)
> m = m+1
> for(k in 1:length(dataset$stdres)){
> dataset$outlier_counter[k] = if(dataset$stdres[k] >= 3 |
> dataset$stdres[k] <= -3)
> {m} else{0}
> }
> j = length(which(dataset$outlier_counter >= 1))
> }
> return(dataset)
> }
> When I pass
> rm.outliers(xsys,fitted.modely1.temp.l)
> fitted.modely1 .temp.l is mylinear model.
> It shows me error like
>
> Error in `$<-.data.frame`(`*tmp*`, "predicted", value = c(0.306726561735386,
> :
>
> replacement has 731 rows, data has 717
When you get a mismatch of "data" and replacement lengths like that it suggests
you have NA values in some of the model variables. If that's the case then the
absence of an example means we are not be able to demonstrate that effect, but
you should be able to make a more modest example and test that hypothesis.
>
> Called from: `$<-`(`*tmp*`, "predicted", value = c(0.306726561735386,
> 0.306726561)
>
>
>
> Help me out
>
>
> Xsys data has 331 rows and 18 column
>
> [[alternative HTML version deleted]]
>
This is a plain text mailing list. Please read the Posting Guide more
thoroughly.
--
David.
>
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David Winsemius
Alameda, CA, USA
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