zyp.trend.vector

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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

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

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.

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).

See Also

zyp.trend.csv, zyp-package, confint.zyp, zyp.sen.

Examples

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

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

# 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)

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