# 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

 ```1 2 3 4``` ```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).

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# 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) ```

### Example output

```Loading required package: Kendall
```

zyp documentation built on May 2, 2019, 6:40 a.m.