stepwise.VT.val {WASP}

Calculate stepwise high order VT in validation

Description

Calculate stepwise high order VT in validation

Usage

stepwise.VT.val(data, J, dwt, mode = c("MRA", "MODWT", "AT"), detrend = FALSE)

Arguments

data

A list of data, including response and predictors

J

Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2).

dwt

Output from dwt.vt(), including the transformation covariance

mode

A mode of variance transformation, i.e., MRA, MODWT, or AT

detrend

Detrend the input time series or just center, default (F)

Value

A list of objects, including transformed predictors

Examples

### Real-world example
data("rain.mon")
data("obs.mon")
mode <- switch(1,
  "MRA",
  "MODWT",
  "a trous"
)
wf <- "d4"
station.id <- 5 # station to investigate
#SPI.12 <- SPEI::spi(rain.mon, scale = 12)$fitted
SPI.12 <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(2009,12)),sc=12)
lab.names <- colnames(obs.mon)
# plot.ts(SPI.12[,1:10])

#--------------------------------------
### calibration
x <- window(SPI.12[, station.id], start = c(1950, 1), end = c(1979, 12))
dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(1979, 12))

data <- list(x = x, dp = matrix(dp, ncol = ncol(dp)))
dwt <- stepwise.VT(data, mode = mode, wf = wf, flag = "biased")
cpy <- dwt$cpy
#--------------------------------------
### validation
x <- window(SPI.12[, station.id], start = c(1980, 1), end = c(2009, 12))
dp <- window(obs.mon[, lab.names], start = c(1980, 1), end = c(2009, 12))

data.n <- list(x = x, dp = matrix(dp, ncol = ncol(dp)))
dwt.val <- stepwise.VT.val(data = data.n, dwt = dwt, mode = mode)

### plot transformed predictor before and after
op <- par(mfrow = c(length(cpy), 1), mar = c(0, 3, 2, 1))
for (i in seq_along(cpy))
{
  ts.plot(cbind(dwt.val$dp[, i], dwt.val$dp.n[, i]), xlab = "NA", col = 1:2)
}
par(op)