plot.lts             package:robustbase             R Documentation

_R_o_b_u_s_t _L_T_S _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:

        1.  a plot of the standardized residuals versus their index,

        2.  a plot of the standardized residuals versus fitted values,

        3.  a Normal Q-Q plot of the standardized residuals, and

        4.  a regression diagnostic plot (standardized residuals versus
           robust distances of the predictor variables).

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

     ## S3 method for class 'lts':
     plot(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: string indicating which plot to show.  See the _Details_
          section for a description of the options.  Defaults to
          '"all"'.

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

     ask: logical indicating if the user should be _ask_ed before each
          plot, see 'par(ask=.)'.  Defaults to 'which == "all" &&
          dev.interactive()'. 

    id.n: number of observations to be identified 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:

     '_r_q_q': Normal Q-Q plot of the standardized residuals;

     '_r_i_n_d_e_x': plot of the standardized residuals versus their index;

     '_r_f_i_t': plot of the standardized residuals versus fitted values;

     '_r_d_i_a_g': 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(Y ~ ., data = hbk)
     lts
     plot(lts, which = "rqq") 

