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Finding the principal components
3 messages · dileep kunjaai, David L Carlson
Check the posting guidelines and give us a small reproducible example using dput(). It is here http://www.R-project.org/posting-guide.html You say you want "PCs of a spatial data set (single variable)", but you must mean something else. It sounds like your variables are highly correlated with one another or you have more variables than cases. The function prcomp also computes PCs but it uses singular value decomposition rather than matrix inversion. ---------------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of dileep kunjaai
Sent: Friday, May 18, 2012 8:01 AM
To: r-help at r-project.org
Subject: [R] Finding the principal components
Dear all,
I am trying to find the PCs of a spatial data set (single
variable). I want to calculate the PCs at each Lat-Lon location.
The* 'princomp'* command gives the approximate standardized
data,
(i.e* pca$scores*), stranded deviation ..etc. I tried*
'pca$loadings'*also, but it giving value 1 all time.
Then I tried manually*(* First calculate correlation matrix
(X*X^T), then arranged it's eigen value in descending order, and chose
the
corresponding eigenvectors (Q_j's), then pc=X^(T)* Q_j , it will give
a
single value called first PC as j=1 *)*, and found PCs but this value
is
different from *'pca$loadings'*.
But I can find the approximate standardized data, (pc1*Q_1)
which is similar to *pca$scores*. But this method is time consuming.
Please help me to tackle this problem.
Thank you for all in advance
--
DILEEPKUMAR. R
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