stepwise.VT: Calculate stepwise high order VT in calibration

In WASP: Wavelet System Prediction

Calculate stepwise high order VT in calibration

Description

Calculate stepwise high order VT in calibration

Usage

stepwise.VT(
  data,
  alpha = 0.1,
  nvarmax = 4,
  mode = c("MRA", "MODWT", "AT"),
  wf,
  J,
  method = "dwt",
  pad = "zero",
  boundary = "periodic",
  cov.opt = "auto",
  flag = "biased",
  detrend = FALSE
)

Arguments

data	A list of data, including response and predictors
alpha	The significance level used to judge whether the sample estimate is significant. A default alpha value is 0.1.
nvarmax	The maximum number of variables to be selected.
mode	A mode of variance transformation, i.e., MRA, MODWT, or AT
wf	Wavelet family
J	Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2).
method	Either "dwt" or "modwt" of MRA.
pad	The method used for extend data to dyadic size. Use "per", "zero", or "sym".
boundary	Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself.
cov.opt	Options of Covariance matrix sign. Use "pos", "neg", or "auto".
flag	Biased or Unbiased variance transformation.
detrend	Detrend the input time series or just center, default (F).

Value

A list of 2 elements: the column numbers of the meaningful predictors (cpy), and partial informational correlation (cpyPIC).

References

Sharma, A., Mehrotra, R., 2014. An information theoretic alternative to model a natural system using observational information alone. Water Resources Research, 50(1): 650-660.

Jiang, Z., Sharma, A., & Johnson, F. (2021). Variable transformations in the spectral domain – Implications for hydrologic forecasting. Journal of Hydrology, 126816.

Examples

### Real-world example
data("rain.mon")
data("obs.mon")
mode <- switch(1,
  "MRA",
  "MODWT",
  "AT"
)
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])

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")

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

WASP documentation built on Sept. 11, 2024, 9:03 p.m.