wapls {paltran} | R Documentation |
This function computes with a given training set and environmental parameter a weighted averaging - partial least square (WA-PLS) transfer function as used in paleolimnology. For error estimation a 10 fold cross validation procedure is choosen: For large data sets (and a high number of components) the calculation time will be 25 seconds and more. The last column of the output of the performance is needed for MW, for simple WA-PLS this can bee ignored.)
wapls(..., n_comp = 4, d.plot = TRUE, plot.comp = "RMSEP", env.trans = FALSE, spec.trans = FALSE, diagno = TRUE)
... |
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 |
n_comp |
number of components that should bee extract |
d.plot |
TRUE/FALSE: if TRUE diagnostic plots are given at the end of the analysis |
plot.comp |
"RMSEP": the diagnostig plots are done automatical for that component with the lowest RMSEP |
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 |
diagno |
should N2,number of non zero values bee calculated for the training set and test set? |
non_z_train |
Number of non zero species in each sample of the training set |
N2_train |
Hill's N2 of each sampel of the training set |
scores |
site scores of the training set samples |
inferred.env_train |
inferred environmental parameter for the training set |
non_z_test |
Number of non zero species in each sample of the test set |
N2_test |
Hill's N2 of each sampel of the test set |
reconstruction |
reconstructed environmental parameter for the samples of the core |
performance |
performance of the wa-pls regression |
Sven Adler, sven.adler2@uni-rostock.de University Rostock, Institute for Biosciences, General and Systematic Botany, Germany
ter Braak, C.J.F. & Juggins, S. 1993. Weighted averaging partial least squares regression WA-PLS: an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269:485-502.
wa, mwtraf, pom, package analogue (Simpson, 2008) for wa and MAT
data(age_dud) data(dud.df) data(train_set.MV) data(train_env.MV) fit1<-wapls(train_set.MV,train_env.MV,dud.df) names(fit1) palplot(fit1$reconstruction[,1],age_dud) palplot(fit1$reconstruction[,1],age_dud,trans="log10")