paleodata_signal_extraction: Extract low-dimensional signals from multivariate datasets...

In paleovar/ptboxproxytools: PTBox proxy tools for time series processing and analysis

Extract low-dimensional signals from multivariate datasets (e.g. pollen assemblages)

Description

Extract low-dimensional signals from multivariate datasets (e.g. pollen assemblages)

Usage

paleodata_signal_extraction(xin, signal_type, signal_components = 1)

Arguments

xin	Proxytibble with multivariate proxy data in 'zoo::zoo' format, or a multivariate irregular time series object ('zoo::zoo')
signal_type	Method to extract signals; Implemented methods: 'pca' Principal component analysis 'dca' Detrended correspondance analysis 'ca' Correspondance analysis 'prc' Principal curves
signal_components	Components that should be returned (e.g. c(2,3) for 'pca' returns the second and third principal components)

Value

Proxytibble with extracted signals in proxy data, or irregular time series object ('zoo::zoo') containing the extracted signals

See Also

prcomp (from 'stats') for principal component analysis

decorana (from 'vegan') for detrended correspondance analysis

cca (from 'vegan') for correspondance analysis

principal_curve (from 'princurve') for principal curves

Examples

# Load ice core example data
library(PTBoxProxydata)
mng <- ProxyDataManager()
icecoredata <- load_set(mng,'icecore_testset',zoo_format = 'zoo')
# Compute PCA from multivariate zoo's
icecoredata_pca <- paleodata_signal_extraction(icecoredata, 'pca')
plot(icecoredata_pca$proxy_data[[1]])
plot(icecoredata_pca$proxy_data[[2]])
# Compute principal curve from multivariate zoo's
icecoredata_prc <- paleodata_signal_extraction(icecoredata, 'prc')
plot(icecoredata_prc$proxy_data[[1]])
plot(icecoredata_prc$proxy_data[[2]])

paleovar/ptboxproxytools documentation built on June 9, 2025, 1:40 a.m.