plotmvoutlier             package:StatDA             R Documentation

_M_u_l_t_i_v_a_r_i_a_t_e _o_u_t_l_i_e_r _p_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     This function plots multivariate outliers. One possibility is to
     distinguish between outlier and no outlier. The alternative is to
     distinguish between the different percentils (e.g. <25%,
     25%<x<50%,...).

_U_s_a_g_e:

     plotmvoutlier(coord, data, quan = 1/2, alpha = 0.025, symb = FALSE, bw = FALSE,
     plotmap = TRUE, map = "kola.background", which.map = c(1, 2, 3, 4),
     map.col = c(5, 1, 3, 4), map.lwd = c(2, 1, 2, 1), pch2 = c(3, 21),
     cex2 = c(0.7, 0.2), col2 = c(1, 1), lcex.fac = 1, ...)

_A_r_g_u_m_e_n_t_s:

   coord: the coordinates for the points 

    data: the value for the different coordinates 

    quan: Number of subsets used for the robust estimation of the
          covariance matrix. Allowed are values between 0.5 and 1., see
          covMcd 

   alpha: Maximum thresholding proportion 

    symb: if FALSE, only two different symbols (outlier and no outlier)
          will be used 

      bw: if TRUE, symbols are in gray-scale (only if symb=TRUE) 

 plotmap: if TRUE, the map is plotted 

     map: the name of the background map 

which.map, map.col, map.lwd: parameters for the background plot, see
          plotbg 

pch2, cex2, col2: graphical parameters for the points 

lcex.fac: factor for multiplication of symbol size (only if symb=TRUE)

     ...: further parameters for the plot

_D_e_t_a_i_l_s:

     The function computes a robust estimation of the covariance and
     then the Mahalanobis distances are calculated. With this distances
     the data set is divided into outliers and non outliers. If
     symb=FALSE only two different symbols are used otherwise different
     grey scales are used to distinguish the different types of
     outliers.

_V_a_l_u_e:

       o: returns the outliers

      md: the square root of the Mahalanobis distance

euclidean: the Euclidean distance of the scaled data

_A_u_t_h_o_r(_s):

     Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     'plotbg', 'covMcd', 'arw'

_E_x_a_m_p_l_e_s:

     data(moss)
     X=moss[,"XCOO"]
     Y=moss[,"YCOO"]
     el=c("Ag","As","Bi","Cd","Co","Cu","Ni")
     x=log10(moss[,el])

     data(kola.background)
     plotmvoutlier(cbind(X,Y),x,symb=FALSE,map.col=c("grey","grey","grey","grey"),
            map.lwd=c(1,1,1,1),
            xlab="",ylab="",frame.plot=FALSE,xaxt="n",yaxt="n")

