Ralph.MacNally@sci.monash.edu.au
2002-Jan-25 04:22 UTC
[Rd] na.action=na.omit doesn't omit in glm() (PR#1282)
Full_Name: Ralph Mac Nally Version: 1.40 OS: MacOS9x/Windows98 Submission from: (NULL) (130.194.13.180) I'm using a simple glm in which the dependent variable may have missing data (coded 'NA') as usual. Both the default na.action (na.omit) and explicit 'na.action=na.omit' do not seem to ignore the missing data as I expect, generating an error to that effect (NA passed to routine). I encounter the same problem in classic Mac and Windows98 versions, but S works alright. What is the source/solution to this problem? Thanks Ralph -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-devel-request@stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Prof Brian D Ripley
2002-Jan-25 07:46 UTC
[Rd] na.action=na.omit doesn't omit in glm() (PR#1282)
On Fri, 25 Jan 2002 Ralph.MacNally@sci.monash.edu.au wrote:> Full_Name: Ralph Mac Nally > Version: 1.40 > OS: MacOS9x/Windows98 > Submission from: (NULL) (130.194.13.180) > > > I'm using a simple glm in which the dependent variable may have missing data > (coded 'NA') as usual. Both the default na.action (na.omit) and explicit > 'na.action=na.omit' do not seem to ignore the missing data as I expect, > generating an error to that effect (NA passed to routine). I encounter the same > problem in classic Mac and Windows98 versions, but S works alright. > > What is the source/solution to this problem?Could we have a reproducible example, please? Based on example(glm) counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12) outcome <- gl(3, 1, 9) treatment <- gl(3, 3) d.AD <- data.frame(treatment, outcome, counts) glm(counts ~ outcome + treatment, family = poisson, data=d.AD) Degrees of Freedom: 8 Total (i.e. Null); 4 Residual Null Deviance: 10.58 Residual Deviance: 5.129 AIC: 56.76 d.AD[1, 1] <- NA glm(counts ~ outcome + treatment, family = poisson, data=d.AD) Degrees of Freedom: 7 Total (i.e. Null); 3 Residual Null Deviance: 10.46 Residual Deviance: 4.011 AIC: 50.91 d.AD[9,3] <- NA glm(counts ~ outcome + treatment, family = poisson, data=d.AD) Degrees of Freedom: 6 Total (i.e. Null); 2 Residual Null Deviance: 8.934 Residual Deviance: 2.725 AIC: 45.28 appears to work exactly as advertised. Or try not using a data frame: counts[9] <- NA glm(counts ~ outcome + treatment, family = poisson) Degrees of Freedom: 7 Total (i.e. Null); 3 Residual Null Deviance: 8.971 Residual Deviance: 3.204 AIC: 50.5 -- Brian D. Ripley, ripley@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-devel-request@stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
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