plot.lts                package:rrcov                R Documentation

_R_o_b_u_s_t _R_e_g_r_e_s_s_i_o_n _D_i_a_g_n_o_s_t_i_c _P_l_o_t_s

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

     Four plots (selectable by 'which') are currently provided: a plot
     of the standardized residuals versus their index, a plot of the
     standardized residuals versus fitted values, a Normal Q-Q plot of
     the standardized residuals, and a regression diagnostic plot
     (standardized residuals versus robust distances of the predictor
     variables).

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

         ## S3 method for class 'lts':
         plot.lts(x, which = c("all","rqq","rindex","rfit","rdiag"), classic=FALSE, ask=(which=="all" && dev.interactive()), id.n, ...)
         

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

      x : a 'lts' object, typically result of 'ltsReg'. 

  which : Which plot to show? See Details for description of the
          options. Default is 'which'="all". 

classic : whether to plot the classical plots too. Default is
          'classic'=FALSE. 

     ask: logical; if 'TRUE', the user is _ask_ed before each plot, see
          'par(ask=.)'.  Default is 'ask = which=="all" &&
          dev.interactive()'.  

   id.n : Number of observations to identify by a label starting with
          the most extreme.  Default is the number of identified
          outliers (can be different for the different plots - see
          Details).  

    ... : other parameters to be passed through to plotting functions. 

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

     This function produces several plots based on the robust and
     classical regression estimates.  Which of them to select is
     specified by the attribute  'which'. The possible options are: 

     'rqq' - Normal Q-Q plot of the standardized residuals;

     'rindex' - plot of the standardized residuals versus their index;

     'rfit' - plot of the standardized residuals versus fitted values;

     'rdiag' - regression diagnostic plot.

     The normal quantile plot produces a normal Q-Q plot of the
     standardized residuals.  A line is drawn which passes through the
     first and third quantile. The id.n residuals,  with largest
     distances from this line are identified by labels (the observation
     number).  The default for id.n is the number of regression
     outliers (lts.wt==0).

     In the Index plot and in the Fitted values plot the standardized
     residuals are  displayed against the observation number or the
     fitted value respectively.  A horizontal dashed line is drawn at 0
     and two solid horizontal lines are  located at +2.5 and -2.5. The
     id.n residuals with largest absolute values are  identified by
     labels (the observation number). The default for id.n is the 
     number regression outliers (lts.wt==0).

     The regression diagnostic plot, introduced by Rousseeuw and van
     Zomeren (1990), displays the standardized residuals versus robust
     distances. Following Rousseeuw and van Zomeren (1990), the
     horizontal dashed lines are located at +2.5 and -2.5  and the
     vertical line is located at the upper 0.975 percent point of the
     chi-squared distribution with p degrees of freedom. The id.n
     residuals  with largest absolute values and/or largest robust
     Mahalanobis distances are  identified by labels (the observation
     number). The default for id.n is the number of all outliers: 
     regression outliers (lts.wt==0) + leverage (bad and good) points
     (RD > 0.975 percent point of the chi-squared distribution with p
     degrees of freedom).

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

     P. J. Rousseeuw and van Zomeren, B. C. (1990). Unmasking
     Multivariate Outliers and Leverage Points. _Journal of the
     American Statistical Association_ *85*, 633-639.

     P. J. Rousseeuw and K. van Driessen (1999)  A fast algorithm for
     the minimum covariance determinant estimator.  _Technometrics_
     *41*, 212-223.

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

     'covPlot'

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

     data(hbk)
     lts <- ltsReg(hbk.x,hbk.y)
     plot(lts, which="rqq")

