glm.diag                package:boot                R Documentation

_G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_o_d_e_l _D_i_a_g_n_o_s_t_i_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     Calculates jackknife deviance residuals, standardized deviance
     residuals,  standardized Pearson residuals, approximate Cook
     statistic, leverage and estimated dispersion.

_U_s_a_g_e:

     glm.diag(glmfit)

_A_r_g_u_m_e_n_t_s:

  glmfit: 'glmfit' is a 'glm.object' - the result of a call to 'glm()' 

_V_a_l_u_e:

     Returns a list with the following components

     res: The vector of jackknife deviance residuals. 

      rd: The vector of standardized deviance residuals. 

      rp: The vector of standardized Pearson residuals. 

    cook: The vector of approximate Cook statistics. 

       h: The vector of leverages of the observations. 

      sd: The value used to standardize the residuals.  This is the the
          estimate of residual standard deviation in the Gaussian
          family and is the square root of the estimated shape
          parameter in the Gamma family.  In all other cases it is 1. 

_N_o_t_e:

     See the help for 'glm.diag.plots' for an example of the use of
     'glm.diag'.

_R_e_f_e_r_e_n_c_e_s:

     Davison, A.C. and Snell, E.J.  (1991)  Residuals and diagnostics. 
      In _Statistical Theory and Modelling: In Honour of Sir David
     Cox_. D.V. Hinkley, N. Reid and E.J. Snell (editors), 83-106. 
     Chapman and Hall.

_S_e_e _A_l_s_o:

     'glm', 'glm.diag.plots', 'summary.glm'

