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>

### References

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

### 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 1.0 Index]