You can look at the components of the output using str and pick out what you want using $ and attr.
idx <- 1:2 z <- lm(as.matrix(iris[idx]) ~., iris[-idx]) str(z) str(summary(z)) On Nov 23, 2007 1:10 PM, Morgan Hough <[EMAIL PROTECTED]> wrote: > Hi Gabor, > > Thanks for your reply. I have it working now. A couple of follow-ups if > I may. > > I have a shell script parsing the output to find the brain areas where > there is a significant effect of diagnosis but its a bit of a hack. I > was wondering whether there are R specific tools for parsing/summarizing > this kind of output. > > Can I apply multiple comparison corrections via lm() or do I need to > apply something on the model output from lm() after? > > Thanks again for your time. > > Cheers, > > -Morgan > > > Gabor Grothendieck wrote: > > Perhaps something like this: > > > > > >> idx <- 1:2 > >> lm(as.matrix(iris[idx]) ~., iris[-idx]) > >> > > > > Call: > > lm(formula = as.matrix(iris[idx]) ~ ., data = iris[-idx]) > > > > Coefficients: > > Sepal.Length Sepal.Width > > (Intercept) 3.682982 3.048497 > > Petal.Length 0.905946 0.154676 > > Petal.Width -0.005995 0.623446 > > Speciesversicolor -1.598362 -1.764104 > > Speciesvirginica -2.112647 -2.196357 > > > > > > > > On Nov 23, 2007 10:09 AM, Morgan Hough <[EMAIL PROTECTED]> wrote: > > > >> Hi there, > >> > >> I am analyzing a table of brain volume measures where each brain area > >> (183 of them) is a column with a label and volume values. I have another > >> table of explanatory variables (age, gender, diagnosis and > >> IntraCranialVol) that I have been using to model the brain volume > >> differences. I have been doing this for single volume measures with no > >> difficulties but I have been unable to apply this across the whole set > >> of brain areas. If I try: > >> > >> lm(y.df, x.df) > >> Error in eval(expr, envir, enclos) : object "Left_Lateral_Ventricle" not > >> found > >> > >> Left_Lateral_Ventricle happens to be the first column label. Does this > >> not work with tables? I have been unable to find any examples. > >> > >> Would you recommend another approach if I was doing this again. The > >> number of columns (brain areas) depends on the parcellation strategy we > >> use so I will probably be reforming these tables again and again. I > >> would like the simplest way to analyze all the brain areas and find > >> where there are significant differences driven mainly by the diagnosis > >> factor. > >> > >> Thanks in advance for your time. > >> > >> Cheers, > >> > >> -Morgan > >> > >> --------------------------------------------------------------------------- > >> Morgan Hough, > >> D.Phil. Student, > >> Oxford University FMRIB Centre > >> > >> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK > >> +44 (0) 1865 222466 (fax 222717) > >> [EMAIL PROTECTED] http://www.fmrib.ox.ac.uk/~mhough > >> > >> ______________________________________________ > >> 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. > >> > >> > > ______________________________________________ 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.
