SVDplot | R Documentation |
The singular value decomposition (SVD) plot of Zhang et al. (2007) captures the changes in the singular columns as the number of curves gradually increases.
SVDplot(object, order = 3, plot = TRUE, plot.type = c("fts", "image"),
mfrow = c(2, 3))
object |
An object of |
order |
Number of Singular Value Decomposition (SVD) components. The maximum order is 4. |
plot |
Is graphical display required? |
plot.type |
Plot functional time series or images? |
mfrow |
Grid of graphics. |
By using the SVD, Zhang et al. (2007) proposed a plot for visualizing patterns
of functional time series. They considered a set of curves as a two-way (p * n)
data matrix, where p
is the total number of covariates and n
is the total number of curves.
The main advantage of this dynamic plot is to visualize both column and row information of a two-way matrix simultaneously, relate the matrix to the corresponding curves, show local variations, and highlight interactions between columns and rows of a two-way matrix.
When plot = TRUE
, it returns a plot.
When plot = FALSE
, it returns the following:
svds |
A number of singular value decomposition ordered by the amount of explained variation. |
reconstruction |
Reconstruction of the original data using the SVD. |
residual |
Residual of the original data. |
MATLAB code is available at https://www.stat.purdue.edu/~lingsong/.
Using the animate package of Grahn(2011), a set of dynamic movies can be created to visualize the changes in singular rows and singular columns.
Han Lin Shang. Please, report bugs and suggestions to hanlin.shang@anu.edu.au
L. Zhang, J. Marron, H. Shen and Z. Zhu (2007) "Singular value decomposition and its visualization", Journal of Computational and Graphical Statistics, 16(4), 833-854.
A. Grahn (2011) "The animate Package", https://ctan.org/pkg/animate?lang=en.
fboxplot
, svd
SVDplot(ElNino_OISST_region_1and2)
SVDplot(ElNino_OISST_region_1and2, plot.type = "image")
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