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Total 7 documents matching your query.

1. Re: [S] RPART (score: 1): Author: John Wallace <jrw@fish.washington.edu>; Date: Tue, 1 Jun 1999 14:40:29 -0700 (PDT); Or, xx_data.frame(A=c(0/0, NA, NaN,1,2),B=c(NA, 0/0, 1, 4, NaN)) any(is.nan(xx)) [1] T xx[is.nan(xx)]_NA any(is.nan(xx)) [1] F -- John Wallace University of Washington ^ ^ ^ Fisheries Research Instit; /archives/html/s-news/1999-06/msg00010.html (8,598 bytes)
2. [S] RPART (score: 1): Author: "wingm" <wingm@ccmail.orst.edu>; Date: Fri, 28 May 1999 15:48:21 -0700; I have a single continuous response variable and multiple continuous and factor explanatory variables. I've been using RPART and TREE to examine relationships between the response variable and the gr; /archives/html/s-news/1999-05/msg00315.html (7,645 bytes)
3. Re: [S] RPART (score: 1): Author: Prof Brian D Ripley <ripley@stats.ox.ac.uk>; Date: Sat, 29 May 1999 08:34:43 +0100 (BST); It is the response that determines the method needed: you want a sum-of-squares criterion, so "anova" (l/case) is appropriate. It is likely that your NAs are really NaNs. Did you import these data? (; /archives/html/s-news/1999-05/msg00320.html (8,615 bytes)
4. [S] RPART (score: 1): Author: "Barker, Chris {GL P~Palo Alto}" <CHRIS.BARKER@roche.com>; Date: Fri, 29 Jan 1999 12:35:56 -0800; I have frequently used RPART without problems. However, I've started to encounter a problem with RPART detecting NAN's in my data even though I don't appear to have any in the variables I'm using. An; /archives/html/s-news/1999-01/msg00202.html (8,060 bytes)
5. Re: [S] RPART (score: 1): Author: Prof Brian D Ripley <ripley@stats.ox.ac.uk>; Date: Fri, 29 Jan 1999 21:18:54 +0000 (GMT); The usual story is that those NAs are really NaNs. You can't tell at the S level, but the .C interface can, and it objects to NaNs unless told not to. Try bpd24[is.na(bpd24)] <- NA etc and see if you; /archives/html/s-news/1999-01/msg00203.html (9,511 bytes)
6. [S] rpart (score: 1): Author: calzola@mailhost.apa.com (Carlos Alzola); Date: Wed, 24 Jun 1998 16:51:47 -0400; I am trying to use rpart for a classification problem with 10,000 observations. The dependent variable has 11 levels and I have 4 predictors: 2 continuous and 2 categorical (with 5 and 4 levels respe; /archives/html/s-news/1998-06/msg00174.html (7,188 bytes)
7. Re: [S] rpart (score: 1): Author: "Atkinson, Beth" <atkinson@mayo.edu> (Beth Atkinson); Date: Wed, 24 Jun 1998 16:17:42 -0500; A few of suggestions: 1) Turn off the cross-validation and surrogate options (maxsurrogate=0, xval=0). That should speed things up. 2) Try using a smaller subset just to make sure you've got the code; /archives/html/s-news/1998-06/msg00175.html (7,675 bytes)

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