plotuniout              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 _f_o_r _e_a_c_h _d_i_m_e_n_s_i_o_n

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

     A multivariate outlier plot for each dimension is produced.

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

     plotuniout(x, symb = FALSE, quan = 1/2, alpha = 0.025, bw = FALSE,
     pch2 = c(3, 1), cex2 = c(0.7, 0.4), col2 = c(1, 1), lcex.fac = 1, ...)

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

       x: dataset 

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

    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, see arw 

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

pch2, cex2, col2: graphical parameters for the points 

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

     ...: further graphical parameters for the plot

_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:

     'arw', 'covMcd'

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

     data(moss)
     el=c("Ag","As","Bi","Cd","Co","Cu","Ni")
     dat=log10(moss[,el])

     ans<-plotuniout(dat,symb=FALSE,cex2=c(0.9,0.1),pch2=c(3,21))

