lm.influence              package:base              R Documentation

_R_e_g_r_e_s_s_i_o_n _D_i_a_g_n_o_s_t_i_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function provides the basic quantities which are used in
     forming a wide variety of diagnostics for checking the quality of
     regression fits.

_U_s_a_g_e:

     lm.influence(lm.obj)

_A_r_g_u_m_e_n_t_s:

  lm.obj: an object as returned by `lm'.

_D_e_t_a_i_l_s:

     The functions listed in See Also give a more direct way of
     computing a variety of regression diagnostics.

_V_a_l_u_e:

     A list containing the following components: 

     hat: a vector containing the diagonal of the ``hat'' matrix.

coefficients: the change in the estimated coefficients which results
          when the i-th case is dropped from the regression is
          contained in the i-th row of this matrix.

   sigma: a vector whose i-th element contains the estimate of the
          residual standard deviation obtained when the i-th case is
          dropped from the regression.

_N_o_t_e:

     The `coefficients' returned by the R version of `lm.influence'
     differ from those computed by S. Rather than returning the
     coefficients which result from dropping each case, we return the
     changes in the coefficients. This is more directly useful in many
     diagnostic measures.

     Note that cases with `weights == 0' are dropped (contrary to the
     situation in S).

_R_e_f_e_r_e_n_c_e_s:

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

_S_e_e _A_l_s_o:

     `summary.lm' for `summary' and related methods;
     `influence.measures',
     `hat' for the hat matrix diagonals,
     `dfbetas', `dffits', `covratio', `cooks.distance', `lm'.

_E_x_a_m_p_l_e_s:

     ## Analysis of the life-cycle savings data given in Belsley, Kuh
     ## and Welsch.
     data(LifeCycleSavings)
     summary(lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi,
                         data = LifeCycleSavings),
             corr = TRUE)
     rstudent(lm.SR)
     dfbetas(lm.SR)
     dffits(lm.SR)
     covratio(lm.SR)

