Description Usage Arguments Value See Also Examples
This function returns the weighted correlation (w-correlation) matrix for functional time series (fts
) objects
that were reconstructed from functional singular spectrum analysis (fssa
) objects.
1 | fwcor(U, groups)
|
U |
An object of class |
groups |
A list or vector of indices which determines the grouping used for the reconstruction in pairwise w-correlations matrix. |
A square matrix of w-correlation values for the reconstructed fts
objects that were built from.
fssa
components
fssa
, freconstruct
, fts
, wplot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## Not run:
## Univariate FSSA Example on Callcenter data
require(Rfssa)
load_github_data("https://github.com/haghbinh/Rfssa/blob/master/data/Callcenter.RData")
## Define functional objects
D <- matrix(sqrt(Callcenter$calls), nrow = 240)
N <- ncol(D)
time <- seq(ISOdate(1999, 1, 1), ISOdate(1999, 12, 31), by = "day")
K <- nrow(D)
u <- seq(0, K, length.out = K)
d <- 22 # Optimal Number of basis elements
## Define functional time series
Y <- fts(list(D), list(list(d, "bspline")), list(u))
Y
plot(Y, mains = c("Sqrt of Call Center Data"))
## Univariate functional singular spectrum analysis
L <- 28
U <- fssa(Y, L)
ufwcor <- fwcor(U = U, groups = list(1, 2, 3))
wplot(W = ufwcor)
## Multivariate W-Correlation Example on Bivariate Satelite Image Data
require(Rfssa)
load_github_data("https://github.com/haghbinh/Rfssa/blob/master/data/Jambi.RData")
## Raw image data
NDVI <- Jambi$NDVI
EVI <- Jambi$EVI
time <- Jambi$Date
## Kernel density estimation of pixel intensity
D0_NDVI <- matrix(NA, nrow = 512, ncol = 448)
D0_EVI <- matrix(NA, nrow = 512, ncol = 448)
for (i in 1:448) {
D0_NDVI[, i] <- density(NDVI[, , i], from = 0, to = 1)$y
D0_EVI[, i] <- density(EVI[, , i], from = 0, to = 1)$y
}
## Define functional objects
d <- 11
D <- list(D0_NDVI, D0_EVI)
B0 <- list(list(d, "bspline")
B1 <- list(d + 4, "fourier"))
U <- list(c(0, 1), c(0, 1))
Y <- fts(D, B0, B1, U)
plot(Y)
U <- fssa(Y = Y, L = 45)
L <- 45
mfwcor <- fwcor(U = U, groups = list(1, 2, 3, 4))
wplot(W = mfwcor)
## End(Not run)
|
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