rolling_autoc

finds lag-1 autocorrelation in a rolling window; can be used to predict resilience (Liu, Gao, &amp; Wang, 2018)

Contains a variety of functions, based around
regime shift analysis of paleoecological data.
Citations:
Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448>
Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L>
Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680)
rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276>
Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>
Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.

Alex H. Room

rshift

Paleoecology Functions for Regime Shift Analysis

Felipe Franco-Gaviria

Dunia H. Urrego

The authors of the dependency Rust crates 

rolling_autoc function

<dl><dt>data</dt>
<dd>The dataframe that will be used.</dd>
<dt>col</dt>
<dd>The column we are measuring change on.</dd>
<dt>l</dt>
<dd>The time interval (no. of columns) used in the autocorrelation.</dd></dl>

Arguments

Rolling autocorrelation — rolling_autoc

<dl>

<dt>data</dt>
<dd>The dataframe that will be used.</dd>


<dt>col</dt>
<dd>The column we are measuring change on.</dd>


<dt>l</dt>
<dd>The time interval (no. of columns) used in the autocorrelation.</dd>

</dl>

rolling_autoc: Rolling autocorrelation

Description

Usage

Value

Arguments