[R] efficient use of lm over a matrix vs. using apply over rows

[Gabor Grothendieck]

In that case, using the example data from the prior response all you need
is:

coef(lm(t(mat) ~ x))


On Sun, Oct 5, 2008 at 1:18 PM, Mark Kimpel <mwkimpel at gmail.com> wrote:
> Sorry for the vagueness of my question, your interpretation, however, was
> spot on. Correct me if I am wrong, but my impression is that apply is a more
> compact way of a for loop, but that the way R handles them computationally
> are the same. In the article I seem to remember, there was a significant
> increase in speed with your second approach, presumably because function
> calls are avoided in R and the heavy lifting is done in C. I will use your
> second approach anyway, but can I expect increased computational efficiency
> with it and, if so, is my reasoning in the prior sentence correct?
>
> BTW, it appears as though my own attempt was almost correct, but I did not
> transpose the matrix. In genomics, our response variables (genes) are the
> rows and the predictor values are the column names. The BioConductor
> packages I routinely use are very good at hiding this and I just didn't come
> to mind.
>
> Mark
> ------------------------------------------------------------
> Mark W. Kimpel MD  ** Neuroinformatics ** Dept. of Psychiatry
> Indiana University School of Medicine
>
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>
> On Sun, Oct 5, 2008 at 10:28 AM, Duncan Murdoch <murdoch at stats.uwo.ca>wrote:
>
>> On 05/10/2008 10:08 AM, Mark Kimpel wrote:
>>
>>> I have a large matrix, each row of which needs lm applied. I am certain
>>> than
>>> I read an article in R-news about this within the last year or two that
>>> discussed the application of lm to matrices but I'll be darned if I can
>>> find
>>> it with Google. Probably using the wrong search terms.
>>>
>>> Can someone steer me to this article of just tell me if this is possible
>>> and, if so, how to do it? My simplistic attempts have failed.
>>>
>>
>> You don't give a lot of detail on what you mean by applying lm to a row of
>> a matrix, but I'll assume you have fixed predictor variables, and each row
>> is a different response vector.  Then you can use apply() like this:
>>
>> x <- 1:10
>> mat <- matrix(rnorm(200), nrow=20, ncol=10)
>>
>> resultlist <- apply(mat, 1, function(y) lm(y ~ x))
>> resultcoeffs <- apply(mat, 1, function(y) lm(y ~ x)$coefficients)
>>
>>
>> "resultlist" will contain a list of 20 different lm() results,
>> "resultcoeffs" will be a matrix holding just the coefficients.
>>
>> lm() also allows the response to be a matrix, where the columns are
>> considered different components of a multivariate response.  So if you
>> transpose your matrix you can do it all in one call:
>>
>> resultmulti <- lm(t(mat) ~ x)
>>
>> The coefficients of resultmulti will match resultcoeffs.
>>
>> Duncan Murdoch
>>
>> Duncan Murdoch
>>
>
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