Deletion Diagnostics           package:car           R Documentation

_D_e_l_e_t_i_o_n _D_i_a_g_n_o_s_t_i_c_s _f_o_r _L_i_n_e_a_r _a_n_d _G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_o_d_e_l_s

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

     These functions calculate a variety of leave-one-out deletion
     diagnostics for
 linear and generalized linear models, including
     studentized residuals (for
 outlier detection), hatvalues (for
     detecting high-leverage observations), and
 Cook's distances,
     dfbeta, and dfbetas (for detecting influential observations).

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



     rstudent(model, ...)

     rstudent.lm(model, infl=influence(model), names=infl$names)

     rstudent.glm(model, infl=influence(model), names=infl$names)

     hatvalues(model, ...)

     hatvalues.lm(model, infl=influence(model), names=infl$names)

     cookd(model, ...)

     cookd.lm(model, infl=influence(model), sumry=summary(model), names=infl$names)

     cookd.glm(model, infl=influence(model), sumry=summary(model), names=infl$names)

     dfbeta(model, ...)

     dfbeta.lm(model, infl=influence(model), names=infl$names)

     dfbetas(model, ...)

     dfbetas.lm(model, infl=influence(model), sumry=summary(model), names=infl$names)

     influence(model, ...)

     influence.lm(model)

     influence.glm(model)


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

   model: `lm' or `glm' model object.

    infl: optionally, an influence-object precomputed for the `model'
          by `influence'.

   sumry: optionally, a summary-object precomputed for the `model' by
          `summary'.

   names: optionally, a vector of observation names.

     ...: arguments to be passed down from generic functions to method
          functions.

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

     Basic quantities are computed by `influence.lm' or
     `influence.glm', which are slightly
 modified versions of
     `lm.influence' from the base package. Values for generalized
     linear
 models are approximations, as described in Williams (1987)
     (except that Cook's distances are
 scaled as F rather than as
     chi-square values).

     Normally, the generic versions of these functions are the ones to
     be used directly. For 
 `hatvalues', `dfbeta', and `dfbetas', the
     method for linear models
 also works for generalized linear
     models. 

     The following diagnostics are provided:

     `_r_s_t_u_d_e_n_t' {studentized residuals.}

     `_h_a_t_v_a_l_u_e_s' {observation leverages.}

     `_c_o_o_k_d' {Cook's distance influence measure for observations.}

     `_d_f_b_e_t_a' {change in each coefficient upon deleting observations.}

     `_d_f_b_e_t_a_s' {standardized change in each coefficient for deleting
          observations.}

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

     `rstudent', `hatvalues', and `cookd' return vectors with one entry
     for
 each observation; `dfbeta' and `dfbetas' return matrices with
     rows for 
 observations and columns for coefficients.

     `influence' returns a list with entries:

   names: observation names.

     hat: hat-values.

   sigma: leave-one-out estimates of linear-model standard error or 
          generalized-linear-model scale.

coefficients: dfbeta values.

  wt.res: weighted residuals (for a linear model).

 dev.res: deviance residuals (for a generalized linear model).

pear.res: Pearson residuals (for a generalized linear model).

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

     John Fox jfox@mcmaster.ca

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

     Belsley, D. A. and Kuh, E. and Welsch, R. E. (1980)
 Regression
     Diagnostics. Wiley.

     Cook, R. D. and Weisberg, S. (1984)
 Residuals and Influence in
     Regression. Wiley.

     Fox, J. (1997)
 Applied Regression, Linear Models, and Related
     Methods. Sage.

     Williams, D. A. (1987)
 Generalized linear model diagnostics using
     the deviance and single
 case deletions. Applied Statistics 36,
     181-191.

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

     `influence.measures'

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


     data(Duncan)
     attach(Duncan)
     mod <- lm(prestige ~ income + education)
     qq.plot(rstudent(mod), distribution="t", df=41)
     plot(hatvalues(mod))
     plot(cookd(mod))
     plot(dfbeta(mod)[,2])
     plot(dfbetas(mod)[,2])

