rg.mva: Non-robust Multivariate Data Analysis

Description

Procedure to undertake non-robust multivariate data analysis. The saved list may be passed to other rotation and display functions

Usage

rg.mva(x, main = deparse(substitute(x)))

Arguments

data

main

used for the list

Value

number of rows

number of columns

wts

the weights for the covariance matrix

mean

the mean of the data

cov

the covariance

the standard deviation

correlation matrix

eigenvalues

eigenvalues of the SVD

econtrib

proportion of eigenvalues in %

eigenvectors

eigenvectors of the SVD

rload

loadings matrix

rcr

standardised loadings matrix

vcontrib

scores variance

pvcontrib

proportion of scores variance in %

cpvcontrib

cummulative proportion of scores variance

Mahalanbois distance

ppm

probability for outliegness using F-distribution

epm

probability for outliegness using Chisquared-distribution

Details

Procesure to undertake non-robust multivariate data analyses; the object generated is identical to that of rg.robmva so that the savedlist may be passed to other rotation and display functions. Thus weights are set to 1, and other variables are set to appropriate defaults. The estimation of Mahalanobis distances is only undertaken if x is nonsingular, i.e. the lowest eigenvalue is > 10e-4.

References

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.

Examples

Run this code

# NOT RUN {
#input data
data(ohorizon)
vegzn=ohorizon[,"VEG_ZONE"]
veg=rep(NA,nrow(ohorizon))
veg[vegzn=="BOREAL_FOREST"] <- 1
veg[vegzn=="FOREST_TUNDRA"] <- 2
veg[vegzn=="SHRUB_TUNDRA"] <- 3
veg[vegzn=="DWARF_SHRUB_TUNDRA"] <- 3
veg[vegzn=="TUNDRA"] <- 3
el=c("Ag","Al","As","B","Ba","Bi","Ca","Cd","Co","Cu","Fe","K","Mg","Mn",
  "Na","Ni","P","Pb","Rb","S","Sb","Sr","Th","Tl","V","Y","Zn")
x <- log10(ohorizon[!is.na(veg),el])
v <- veg[!is.na(veg)]

rg.mva(as.matrix(x[v==1,]))

# }

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