[R] applying lm on an array of observations with common design matrix
Petr Klasterecky
klaster at karlin.mff.cuni.cz
Thu Feb 22 08:04:30 CET 2007
Ranjan Maitra napsal(a):
> On Sun, 18 Feb 2007 07:46:56 +0000 (GMT) Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
>
>> On Sat, 17 Feb 2007, Ranjan Maitra wrote:
>>
>>> Dear list,
>>>
>>> I have a 4-dimensional array Y of dimension 330 x 67 x 35 x 51. I have a
>>> design matrix X of dimension 330 x 4. I want to fit a linear regression
>>> of each
>>>
>>> lm( Y[, i, j, k] ~ X). for each i, j, k.
>>>
>>> Can I do it in one shot without a loop?
>> Yes.
>>
>> YY <- YY
>> dim(YY) <- c(330, 67*35*51)
>> fit <- lm(YY ~ X)
>>
>>> Actually, I am also interested in getting the p-values of some of the
>>> coefficients -- lets say the coefficient corresponding to the second
>>> column of the design matrix. Can the same be done using array-based
>>> operations?
>> Use lapply(summary(fit), function(x) coef(x)[3,4]) (since there is a
>> intercept, you want the third coefficient).
>
> In this context, can one also get the variance-covariance matrix of the coefficients?
Sure:
lapply(summary(fit), function(x) {"$"(x,cov.unscaled)})
Add indexing if you do not want the whole matrix. You can extract
whatever you want, just take a look at ?summary.lm, section Value.
Petr
--
Petr Klasterecky
Dept. of Probability and Statistics
Charles University in Prague
Czech Republic
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