# fwcor: Weighted Correlation Matrix In Rfssa: Functional Singular Spectrum Analysis

 fwcor R Documentation

## Weighted Correlation Matrix

### Description

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.

### Usage

```fwcor(U, groups)
```

### Arguments

 `U` An object of class `fssa`. `groups` A list or vector of indices which determines the grouping used for the reconstruction in pairwise w-correlations matrix.

### Value

A square matrix of w-correlation values for the reconstructed `fts` objects that were built from. `fssa` components

`fssa`, `freconstruct`, `fts`, `wplot`

### Examples

```## Not run:

## Univariate FSSA Example on Callcenter data
require(Rfssa)
## Define functional objects
D <- matrix(sqrt(Callcenter\$calls), nrow = 240)
N <- ncol(D)
time <- substr(seq(ISOdate(1999, 1, 1), ISOdate(1999, 12, 31), by = "day"),1,10)
K <- nrow(D)
u <- seq(0, K, length.out = K)
d <- 22 # Optimal Number of basis elements
## Define functional time series
Y <- Rfssa::fts(list(D), list(list(d, "bspline")), list(u),time)
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)
## 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)
B <- list(list(d, "bspline"), list(d + 4, "fourier"))
U <- list(c(0, 1), c(0, 1))
Y <- Rfssa::fts(D, B, U, time)
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)

```

Rfssa documentation built on Sept. 9, 2022, 1:07 a.m.