StatDA: rg.robmva – R documentation

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rg.robmva

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://cstat.tuwien.ac.at/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]))

StatDA

Statistical Analysis for Environmental Data

v1.7.4

GPL (>= 3)

Authors

Peter Filzmoser

Initial release

2020-03-10