wapls {paltran}R Documentation

weighted averaging - partial least square (wa-pls) regression for paleoecology

Description

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.)

Usage

wapls(..., n_comp = 4, d.plot = TRUE, plot.comp = "RMSEP", env.trans = FALSE, spec.trans = FALSE, diagno = TRUE)

Arguments

... 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?

Value

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

Author(s)

Sven Adler, sven.adler2@uni-rostock.de University Rostock, Institute for Biosciences, General and Systematic Botany, Germany

References

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.

See Also

wa, mwtraf, pom, package analogue (Simpson, 2008) for wa and MAT

Examples

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")


[Package paltran version 1.0-0 Index]