mwtraf {paltran} | R Documentation |
The moving window method identifies the best number of nearest neighbours (window size) for each fossil diatom assemblage and the best transfer function based on the error statistic for each sample of the test set (e.g. sediment core)
mwtraf(..., method = "wapls", rplot = "TRUE", mwsize = c(40, 60, 80, 100, 120, 140, 160), mw_type = "dca", ncomp = 4, rmsep.incl = TRUE, env.trans = FALSE, spec.trans = FALSE)
... |
x,y,z: required: species training set (x) as matrix and related environmental parameter (y). optional: test set(z) - species data from a sediment core. For all data: row- and columnnames are required! Otherwize the sample specific training set and the related environmenal parameter can not be found! |
method |
currently only wapls is running: choosing ncomp=1 a simple wa is done, for each sample specific training set. |
rplot |
TRUE/FALSE should the results plotted? |
mwsize |
vector of the number of samples within each window: default runs only with large data sets, e.g. combined TP data set from the EDDI data base |
mw_type |
How should the nearest neighborous be found? curently only a dca is running, chord distance and NMDS is in preperation. |
ncomp |
number of components that should bee extract using wa-pls |
rmsep.incl |
should the RMSEP used for modelselection? |
env.trans |
should the environmental parameter bee transformed? "sqrt" for square root and "log10" for logarithm to the basis 10 are possible choices, default is FALSE. |
spec.trans |
should the species data bee transformed? "sqrt" for square root and "log10" for logarithm to the basis 10 are possible choices, default is FALSE. |
WARNINGS: One run of a WA-PLS with a normal sample size takes about 20 seconds. Using mwtraf, for each sample 7 WA-PLS runs (default) are calculated, that takes 140 seconds. The reconstruction for a whole sedimet (80-100 samples) core can take 5 h and more! Please try first wih a smal test set, before running the whole reconstruction! Data must be organised in the same way as running da cca or dca in package vegan! (see examples) This approach needs large training sets, like the combinded TP data set from EDDI.
sample.performance |
perforamnce of the sample specific wa-pls model |
reconstruction |
reconstructed environmental parameter |
choosing mwsize=30 and ncomp =1 this approach equals the locally weighted WA regression (Battarby et al. 2006).
Sven Adler, sven.adler2@uni-rostock.de, University Rostock, Institute for Biosciences, General and Systematic Botany, Germany
Huebener, T., Dressler,M., Schwarz,A.,Langner, K., Adler,S. 2007. Dynamic adjustment of training sets (`moving windows` reconstruction) by using transfer functions in paleolimnology -a new approache, J. o. Paleolimnology. DOI 10.2007/s10933-007-9145-7
wa, wapls, wa package analogue
data(dud.df) data(train_set.MV) data(train_env.MV) fit1<-mwtraf(train_set.MV,train_env.MV,dud.df[1:3,],mwsize = c(40, 60, 80)) fit1