| Type: | Package |
| Title: | Paleoecology Functions for Regime Shift Analysis |
| Version: | 3.1.2 |
| Description: | 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/. |
| Suggests: | R.rsp |
| VignetteBuilder: | R.rsp |
| Depends: | R (≥ 4.2) |
| Imports: | grid, tibble, dplyr, ggplot2 |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| SystemRequirements: | rustc & cargo if building from source |
| URL: | https://github.com/alexhroom/rshift |
| BugReports: | https://github.com/alexhroom/rshift/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.1 |
| Config/rextendr/version: | 0.3.1.9001 |
| Packaged: | 2025-04-06 18:15:25 UTC; alexhroom |
| Author: | Alex H. Room 
[aut, cre, cph],
Felipe Franco-Gaviria
[ctb, fnd],
Dunia H. Urrego
[ctb, fnd],
The authors of the dependency Rust crates [ctb] (see inst/AUTHORS file
for details) |
| Maintainer: | Alex H. Room <alexhroom+cran@protonmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-06 18:40:02 UTC |
Hellinger transform
Description
Hellinger transforms data (Legendre and Legendre, Numerical Ecology)
Usage
Hellinger_trans(data, col, site)
Arguments
data |
The dataframe to be used. |
col |
The column that change is being measured on. |
site |
The column containing the site of each sample. |
Value
The ‘data' dataframe with an added ’hellinger_trans_vals' column.
Pacific Decadal Oscillation in January
Description
A dataset containing January PDO values. Subset of the data from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.
Usage
data(January_PDO)
Format
A data frame with 104 rows and 2 variables
Details
PDO - Pacific Decadal Oscillation in January for the given year.
Age - the year for which the PDO was measured.
Lanzante L-test
Description
performs the L-method for detection of regime shifts (Lanzante, 1996)
Usage
Lanzante(data, col, time, p = 0.05, merge = FALSE)
Arguments
data |
The dataframe to be used. |
col |
The column we are measuring change on. |
time |
The column containing time units (e.g. age of a subsample) |
p |
The largest p-value you want to check regime shifts for. Defaults to p = 0.05. |
merge |
Sets the result to be either a regime-shift only table (if FALSE), or an addition to the original table (if TRUE) |
Value
If merge = FALSE (default), produces a 2-column table of time (the time value for each regime shift) and p (the p-value for each regime shift). If merge = TRUE, returns the original dataset with an extra p-value column, giving the p-value for each time unit - 0 for non-shift years.
Examples
Lanzante(lake_data, "DCA1", "Age")
Lanzante(lake_data, "DCA1", "Age", p=0.10, merge=TRUE)
Regime Shift Index graph
Description
creates two graphs, one of data and one of the RSI, as seen in Rodionov (2004)
Usage
RSI_graph(data, col, time, rsi, mean_lines = FALSE)
Arguments
data |
The dataframe that will be used. |
col |
The column we are measuring change on. |
time |
The column containing time units (e.g. age of a subsample) |
rsi |
The column containing RSI values (for best visualisation use Rodionov() with merge=TRUE) |
mean_lines |
If true, add lines over the data indicating the mean of each regime. |
Value
Two graphs, one on top of the other; one of col against time and one of RSI against time.
Examples
RSI_graph(lake_RSI, "DCA1", "Age", "RSI")
Rodionov (2004)'s STARS algorithm
Description
performs STARS analysis (Rodionov, 2004) on a dataset
Usage
Rodionov(data, col, time, l, prob = 0.05, startrow = 1, merge = FALSE)
Arguments
data |
The dataframe to be used. |
col |
The column we are measuring change on. |
time |
The column containing time units (e.g. age of a subsample) |
l |
The cut-off length of a regime; affects sensitivity (see Rodionov, 2004) |
prob |
The p-value for significance of a regime shift. Defaults to p = 0.05. |
startrow |
What row the analysis starts at. Defaults to 1. |
merge |
Sets the result to be either a regime-shift only table (if FALSE), or an addition to the original table (if TRUE) |
Value
If merge = FALSE (default), produces a 2-column table of time (the time value for each regime shift) and RSI (the regime shift index for each regime shift). If merge = TRUE, returns the original dataset with an extra RSI column, giving the regime shift index for each time unit - 0 for non-shift years.
Examples
Rodionov(lake_data, "DCA1", "Age", l=5)
Rodionov(lake_data, "DCA1", "Age", l=5, prob=0.01, startrow=2, merge=TRUE)
Converts absolute abundance data to a percentage of total abundance for each site
Description
Converts absolute abundance data to a percentage of total abundance for each site
Usage
absolute_to_percentage(data, col, site)
Arguments
data |
The dataframe to be used. |
col |
The column that change is being measured on. |
site |
The column containing the site of each sample. |
Value
The ‘data' dataframe with an added ’percentage' column.
DCA-ordinated pollen data from Lake Consuelo with RSI values
Description
A dataset containing pre-processed DCA-ordinated data from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>. This data has been processed using Rodionov(lake_data, "DCA1", "Age", l=5, merge=TRUE)
Usage
data(lake_RSI)
Format
A data frame with 39 rows and 3 variables
Details
DCA1 - DCA values for each timepoint from the raw dataset.
Age - timepoint of each sample that has been DCA-ordinated.
RSI - Regime Shift Index (see docs for Rodionov()) for each timepoint.
DCA-ordinated pollen data from Lake Consuelo
Description
A dataset containing pre-processed DCA-ordinated data from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>.
Usage
data(lake_data)
Format
A data frame with 39 rows and 2 variables
Details
DCA1 - DCA values for each timepoint from the raw dataset.
Age - timepoint of each sample that has been DCA-ordinated.
Calculate means for each regime
Description
calculates the mean for each regime in a regime shift analysis.
Usage
regime_means(data, col, rsi)
Arguments
data |
The dataframe that will be used. |
col |
The column we are measuring change on. |
rsi |
The column containing RSI values. |
Value
A vector of the mean value for each regime.
Examples
regime_means(lake_RSI, "DCA1", "RSI")
Rolling autocorrelation
Description
finds lag-1 autocorrelation in a rolling window; can be used to predict resilience (Liu, Gao, & Wang, 2018)
Usage
rolling_autoc(data, col, l)
Arguments
data |
The dataframe that will be used. |
col |
The column we are measuring change on. |
l |
The time interval (no. of columns) used in the autocorrelation. |
Value
A table of rolling lag-1 autocorrelation values.
Calculates the mean for each regime in a regime shift analysis.
Description
Calculates the mean for each regime in a regime shift analysis.
Usage
rust_regime_means(col, rsi)
Arguments
col |
The column we are measuring change on. |
rsi |
The column containing RSI values. |
Calculate STARS RSI points and return to R as a vector
Description
Calculate STARS RSI points and return to R as a vector
Usage
rust_rodionov(vals, t_crit, l)
Arguments
vals |
The column we are measuring change on |
t_crit |
The critical value of a t-distribution at the desired p-value |
l |
The cut-off length of a regime; affects sensitivity |