Hi Carol, I unsuccessfully tried to get credits right for the following quote (and they make a lot of fuzz about having citations right around here), so I have to stick with the plain line: "Statistics means never having to say you're certain".
Bioconductor has a mailing list on its own, there might be more qualified advice. Best. Am 18.08.2011 14:18, schrieb carol white: > Thanks Eik for your reply. > > Sure I know the classification method. However, at this stage, I'm working on > feature selection. So lmfit and eBays in Limma are more preferable than anova > in stats? > > > Best wishes, > > Haleh > > > > ----- Original Message ----- > From: Eik Vettorazzi <e.vettora...@uke.uni-hamburg.de> > To: carol white <wht_...@yahoo.com> > Cc: > Sent: Thursday, August 18, 2011 10:56 AM > Subject: Re: [R] too many var in lm > > Hi Carol, > methods for classifying observations are legion, starting from logistic > regression, discriminant analysis, CART, (hierarchical) cluster analysis... > > When it comes to analysing gene expressions, www.bioconductor.org might > be the place to visit, especially the limma-package might be promising. > > Regards, > Eik > > > Am 18.08.2011 10:30, schrieb carol white: >> Thanks Eik for your reply. >> >> I have seen that one way to select variables discriminating 2 categories of >> patients is anova. I saw that the anova function should be applied to an >> object like the one obtained from lm. so that's why I wanted to apply >> anova(lm(y~.)). Would you have any suggestions, comments? >> >> Regards, >> >> Carol >> >> >> >> ----- Original Message ----- >> From: Eik Vettorazzi <e.vettora...@uke.uni-hamburg.de> >> To: carol white <wht_...@yahoo.com> >> Cc: "r-h...@stat.math.ethz.ch" <r-h...@stat.math.ethz.ch> >> Sent: Wednesday, August 17, 2011 3:39 PM >> Subject: Re: [R] too many var in lm >> >> Hi Carol, >> it might be another question if it is sensible to use 2100 regression >> parameters, but you can use . to regress one response against all other >> variables in a data frame as in: >> >> lm(formula = mpg ~ ., data = mtcars) >> >> and you can even exclude specific variables using "-" >> lm(formula = mpg ~ . - wt, data = mtcars) >> >> cheers. >> >> Am 17.08.2011 15:23, schrieb carol white: >>> Hello, >>> It might be an easy question but if you have many variables to fit in the >>> lm function, how do you take all without specifying var1+var2+...+var2100 >>> in the terms parameter in response ~ terms? >>> >>> Cheers, >>> >>> Carol >>> >>> ______________________________________________ >>> 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. >> > -- Eik Vettorazzi Department of Medical Biometry and Epidemiology University Medical Center Hamburg-Eppendorf Martinistr. 52 20246 Hamburg T ++49/40/7410-58243 F ++49/40/7410-57790 ______________________________________________ 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.
