zyp.trend.vector: zyp.trend.vector

In zyp: Zhang + Yue-Pilon trends package

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("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

# 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)

zyp documentation built on May 29, 2017, 9:21 p.m.

zyp.trend.csv

zyp index