README

fxTWAPLS: An Improved Version of WA-PLS

Overview

The goal of this package is to provide an improved version of WA-PLS by including the tolerances of taxa and the frequency of the sampled climate variable. This package also provides a way of leave-out cross-validation that removes both the test site and sites that are both geographically close and climatically close for each cycle, to avoid the risk of pseudo-replication.

Installation

You can install the released version of fxTWAPLS from CRAN with:

install.packages("fxTWAPLS")

And the development version from GitHub with:

install.packages("remotes")
remotes::install_github("special-uor/fxTWAPLS", "dev")

Publications

Liu Mengmeng, Prentice Iain Colin, ter Braak Cajo J. F., Harrison Sandy P.. 2020 An improved statistical approach for reconstructing past climates from biotic assemblages. Proc. R. Soc. A. 476: 20200346. https://doi.org/10.1098/rspa.2020.0346 - fxTWAPLS v0.0.2

install.packages("remotes")
remotes::install_github("special-uor/fxTWAPLS@v0.0.2")

Notes

The following functions can be executed in parallel:

To do so, include the cpus parameter. For example:

# without fx
cv_Tmin <- fxTWAPLS::cv.w(taxa,
                          modern_pollen$Tmin,
                          nPLS = 5,
                          fxTWAPLS::WAPLS.w,
                          fxTWAPLS::WAPLS.predict.w,
                          cpus = 2)

Optionally, a progress bar can be displayed for long computations. Just “pipe” the function call to fxTWAPLS::pb().

# without fx
`%>%` <- magrittr::`%>%`
cv_Tmin <- fxTWAPLS::cv.w(taxa,
                          modern_pollen$Tmin,
                          nPLS = 5,
                          fxTWAPLS::WAPLS.w,
                          fxTWAPLS::WAPLS.predict.w,
                          cpus = 2) %>%
  fxTWAPLS::pb()

Alternatively, if you are not familiar with the “pipe” operator, you can run the following code:

# without fx
cv_Tmin <- fxTWAPLS::pb(fxTWAPLS::cv.w(taxa,
                                       modern_pollen$Tmin,
                                       nPLS = 5,
                                       fxTWAPLS::WAPLS.w,
                                       fxTWAPLS::WAPLS.predict.w,
                                       cpus = 2))