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
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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>
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