zyp.trend.vector: zyp.trend.vector

Description

Computes a prewhitened linear trend on a vector of data. The 'zyp' package allows you to use either Zhang's method, or the Yue Pilon method of computing nonlinear prewhitened trends.

Usage

zyp.trend.vector(y, x=1:length(y), method=c("zhang", "yuepilon"),
conf.intervals=TRUE, preserve.range.for.sig.test=FALSE)
zyp.zhang(y, x=1:length(y), conf.intervals=TRUE, preserve.range.for.sig.test=FALSE)
zyp.yuepilon(y, x=1:length(y), conf.intervals=TRUE, preserve.range.for.sig.test=FALSE)

Value

A vector containing the trend and associated data.

lbound: the lower bound of the trend's confidence interval.
trend: the Sen's slope (trend) per unit time.
trendp: the Sen's slope (trend) over the time period.
ubound: the upper bound of the trend's confidence interval.
tau: Kendall's tau statistic computed on the final detrended timeseries.
sig: Kendall's P-value computed for the final detrended timeseries.
nruns: the number of runs required to converge upon a trend.
autocor: the autocorrelation of the final detrended timeseries.
valid_frac: the fraction of the data which is valid (not NA) once autocorrelation is removed.
linear: the least squares fit trend on the same dat.
intercept: the intercept of the Sen's slope (trend).
lbound_intercept: the lower bound of the estimate of the intercept of the Sen's slope (trend).
ubound_intercept: the upper bound of the estimate of the intercept of the Sen's slope (trend).

Arguments

y: vector of input data.
x: vector of time data (optional).
method: the prewhitened trend method to use.
conf.intervals: whether to compute a 95 percent confidence interval based on all possible slopes.
preserve.range.for.sig.test: whether to re-inflate values by dividing by (1 - ac) following removal of autocorrelation prior to computation of significance.

Details

This routine computes a prewhitened nonlinear trend on a vector of data, using either Zhang's (described in Wang and Swail, 2001) or Yue Pilon's (describe in Yue Pilon, 2002) method of prewhitening and Sen's slope, and use a Kendall test for significance.

Examples

Run this code

# Without confidence intervals, using the wrapper routine
d <- zyp.trend.vector(c(0, 1, 3, 4, 2, 5), conf.intervals=FALSE)

# With confidence intervals, using the wrapper routine
d <- zyp.trend.vector(c(0, 1, 3, 4, 2, 5))

# With confidence intervals, not using the wrapper routine
d.zhang <- zyp.zhang(c(0, 1, 3, 4, 2, 5))
d.yuepilon <- zyp.yuepilon(c(0, 1, 3, 4, 2, 5))

# With confidence intervals, with time data.
t.dat <- c(0, 0.3, 1, 3, 3.4, 6)
d <- zyp.trend.vector(c(0, 1, 3, 4, 2, 5), t.dat, method="yuepilon")
d.zhang <- zyp.zhang(c(0, 1, 3, 4, 2, 5), t.dat)
d.yuepilon <- zyp.yuepilon(c(0, 1, 3, 4, 2, 5), t.dat)