detrend_ts: Detrend Time Series

In EWSmethods: Forecasting Tipping Points at the Community Level

Detrend Time Series

Description

Removes directional signals from time series using loess, linear regression or gaussian detrending.

Usage

detrend_ts(data, method = "linear", bandwidth = NULL, span = 0.25, degree = 2)

Arguments

data	The dataframe to be detrended. The first column must be a vector of dates with all other columns the individual time series.
method	The method of detrending. Options include "linear" (residuals of a linear regression), loess (smoothing by local polynomial regression), gaussian (smoothing by a gaussian kernel), or first.difference.
bandwidth	If method = "gaussian", dictates the bandwidth of the gaussian kernel. If NULL, this is estimated from the data.
span	If method = "loess", controls the degree of smoothing as a proportion of points to be used (if span = 1, all points are used)
degree	If method = "loess", specifies the degree polynomials allowed. Options are normally 1 or 2.

Value

Dataframe of deseasoned time series.

Examples

#Generate five random monthly time series
#of 5 years length.

spp_data <- matrix(nrow = 5*12, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
spp_data[,x] <- rnorm(5*12,mean=20,sd=5)})
multi_spp_data <- cbind("time" =
 seq(as.Date('2000/01/01'), as.Date('2004/12/01'), by="month"),
   as.data.frame(spp_data))

detrend_dat <- detrend_ts(data = multi_spp_data,
method = "gaussian",
bandwidth = 2)

EWSmethods documentation built on May 29, 2024, 5:41 a.m.