Mental Block with PCA of multivariate time series!
Is this along the lines of what you are trying to do?
sim.data<-data.frame(matrix(rnorm(350*10),350,10))
day<-seq(1:350)
sim.data<-data.frame(day,sim.data)
pc1.load.all<-NULL
pc2.load.all<-NULL
for (i in seq(0,300,by=50)){
sim.data.i<-subset(sim.data,sim.data$day>i&sim.data$day<(i+50))
pc1.load.i<-princomp(sim.data.i[,2:11])$loadings[,1]
pc2.load.i<-princomp(sim.data.i[,2:11])$loadings[,2]
pc1.load.all<-rbind(pc1.load.all,pc1.load.i)
pc2.load.all<-rbind(pc1.load.all,pc1.load.i)
}
period<-seq(1:7)
pc1.load.all<-cbind(period,pc1.load.all)
pc2.load.all<-cbind(period,pc1.load.all)
# and plot loadings for each each variable vs. the period...
Ignacio
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Laura Quinn
Sent: Monday, May 16, 2005 4:34 AM
To: Gavin Simpson
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Mental Block with PCA of multivariate time series!
Sorry, I don't think I made myself clear enough with my initial query!
I am wishing to investigate the temporal evolution of the pca: if we
assume that every 50 rows of my data frame is representitive of, for
instance, 1 day of data, I am hoping to automate a process whereby a pca
is performed on every 50 rows of data and the loading for PC1 and PC2 for
each variable (i.e. each column) is represented as a point on a plot - so
a years' data will be represented as two lines (representing PC1 and PC2)
on a time series plot for each variable.
Laura Quinn
Institute of Atmospheric Science
School of Earth and Environment
University of Leeds
Leeds
LS2 9JT
tel: +44 113 343 1596
fax: +44 113 343 6716
mail: laura at env.leeds.ac.uk
On Mon, 16 May 2005, Gavin Simpson wrote:
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 -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% 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/ 26 Bedford Way [W] http://www.ucl.ac.uk/~ucfagls/ London. WC1H 0AP. %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
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