TrendUncertainty {greenbrown} R Documentation

## Compute uncertainties in trend statistics according to different start and end dates

### Description

The function computes trend statistics (linear trend slope and intercept, Mann-Kendall tau and p-value) with associated uncertainties (standard deviation) by sampling the time series according to different start and end dates using the function `TrendSample`

### Usage

```TrendUncertainty(Yt, seg = NULL, bp = NoBP(), sample.method = c("sample",
"all", "none"), sample.min.length = 0.75, sample.size = 30,
fun.unc = NULL, trend = TrendAAT)```

### Arguments

 `Yt` univariate time series of class `ts` `seg` a vector indicating segments of a time series. If NULL, provide bp `bp` detected breakpoints in the time series as returned by `breakpoints` `sample.method` Sampling method for combinations of start and end dates to compute uncertainties in trends. If "sample" (default), trend statistics are computed for a sample of combinations of start and end dates according to `sample.size`. If "all", trend statistics are computed for all combinations of start and end dates longer than `sample.min.length`. If "none", trend statistics will be only computed for the entire time series (i.e. no sampling of different start and end dates). `sample.min.length` Minimum length of the time series (as a fraction of total length) that should be used to compute trend statistics. Time windows between start and end that are shorter than min.length will be not used for trend computation. `sample.size` sample size (number of combinations of start and end dates) to be used if `method` is sample. `fun.unc` function to summarize the uncertainty of the trend (default: quantile 0.025 and 0.975). Can be also 'sd' or other functions. `trend` method that should be used to compute the trend

### Value

The function returns a data.frame with the estimated Mann-Kendall tau, p-value and slope and intercept of a linear trend with uncertainties defined as the standard deviation of these estimates dependent on different start and end dates.

### Author(s)

Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]

`Trend`

### Examples

```# load a time series of NDVI (normalized difference vegetation index)
data(ndvi)

# aggregate time series to annual time steps
ndvi <- aggregate(ndvi, FUN=mean, na.rm=TRUE)
plot(ndvi)

# compute trend statistics dependent on start and end of the time series
trd.ens <- TrendSample(ndvi)
plot(trd.ens)

# compute statistics for trend
TrendUncertainty(ndvi)

# compute trend statistics with uncertainties by considering breakpoints
bp <- breakpoints(ndvi ~ time(ndvi))
trd.unc <- TrendUncertainty(ndvi, bp=bp)
trd.unc
trd.unc[[1]]\$slope_unc

```

[Package greenbrown version 2.4.3 Index]