[R] matrix (column-wise) multiple regression

[Gabor Grothendieck]

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 <mhough at fmrib.ox.ac.uk> 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)
> mhough at fmrib.ox.ac.uk    http://www.fmrib.ox.ac.uk/~mhough
>
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