Message-ID: <4288647F.8090409@ucl.ac.uk>
Date: 2005-05-16T09:14:39Z
From: Gavin Simpson
Subject: Mental Block with PCA of multivariate time series!
In-Reply-To: <Pine.LNX.4.44.0505160821450.2392-100000@gw.env.leeds.ac.uk>
Laura Quinn wrote:
> Please could someone point me in the right direction as I appear to be
> having a total mental block with fairly basic PCA problem!
>
> I have a large dataframe where rows represent independent
> observations and columns are variables. I am wanting to perform PCA
> sequentially on blocks of nrows at a time and produce a graphical output
> of the loadings for the first 2 EOFs for each variable.
>
> I'm sure I've performed a very similar routine in the past, but the method
> is currently escaping me.
>
> Any help gratefully received!
Hi Laura,
data(iris)
iris.dat <- iris[,1:4]
pca.1 <- prcomp(iris.dat[1:50, ], scale = TRUE)
pca.2 <- prcomp(iris.dat[51:100, ], scale = TRUE)
pca.3 <- prcomp(iris.dat[100:150, ], scale = TRUE)
biplot(pca.1)
etc...
There is a better way of subsetting this data set as the 5th col of iris
is a factor and we could use the subset argument to prcomp to do the
subsetting without having to know that there are 50 rows per species.
Take a look at that argument if you have a variable that defines the
blocks for you.
Is this what you were after?
All the best,
Gav
--
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Gavin Simpson [T] +44 (0)20 7679 5522
ENSIS Research Fellow [F] +44 (0)20 7679 7565
ENSIS Ltd. & ECRC [E] gavin.simpsonATNOSPAMucl.ac.uk
UCL Department of Geography [W] http://www.ucl.ac.uk/~ucfagls/cv/
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