RT_pca {RTisean} | R Documentation |
PCA
Description
Performs a global SVD.
Usage
RT_pca(series,l,x=0,c=1,m=c(1,2),d=1, W=0, q)
Arguments
series |
a vector or a matrix |
l |
number of data to be used |
x |
number of lines to be ignored |
c |
column to be read |
m |
no. of input columns, embedding dimension |
d |
delay |
W |
an integer code between 0 and 3 indicating the kind of output to be produced (see value section) |
q |
meaning depends on W .
W=2 : Number of components written.
W=3 : Projection dimensiondimensions to write the time series down to |
Value
Depends on the W
option.
0 |
the vector of eigenvalues |
1 |
matrix of eigenvectors. The columns of the output matrix are the eigenvectors |
2 |
Transformation of the time series onto the eigenvector basis. The number of components printed is determined by the q option |
3 |
Projection of the time series onto the first q eigenvectors (global noise reduction) |
See Also
pc
Examples
## Not run:
dat<-henon(100)
svdout<-RT_pca(dat, W=3, q=1)
plot(svdout,t="l",xlab="Time",ylab="Projected Time series")
## End(Not run)
[Package
RTisean version 3.0.10
Index]