rg.robmva {StatDA}R Documentation

Robust Multivariate Analysis

Description

Procedure for multivariate analysis using the minimum volume ellipsoid (MVE), minimum covariance determinant (MCD) or a supplied set of 0-1 weights.

Usage

rg.robmva(x, proc = "mcd", wts = NULL, main = deparse(substitute(x)))

Arguments

x data
proc procedure for the estimation (MVE or MCD)
wts if proc=NULL, the supplied weights for the calculation
main input for the list

Details

cov.mcd is limited to a maximum of 50 variables. Both of these procedures lead to a vector of 0-1 weights and mcd is the default. A set of weights can be generated by using Graphical Adaptive Interactive Trimming (GAIT) procedure available though rg.md.gait(). Using 0-1 weights the parameters of the background distribution are estimated by cov.wt(). A robust estimation of the Mahalanobis distances is made for the total data set but is only undertaken if x is non-singular (lowest eigenvalue is >10e-4).

Value

n number of rows
p number of columns
wts the weights for the covariance matrix
mean the mean of the data
cov the covariance
sd the standard deviation
r 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
md Mahalanbois distance
ppm probability for outliegness using F-distribution
epm probability for outliegness using Chisquared-distribution

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at> http://www.statistik.tuwien.ac.at/public/filz/

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

#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)]
subvar=c("Ag","B","Bi","Mg","Mn","Na","Pb","Rb","S","Sb","Tl")
set.seed(100)

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

[Package StatDA version 1.1 Index]