summary.glm              package:stats              R Documentation

_S_u_m_m_a_r_i_z_i_n_g _G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_o_d_e_l _F_i_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     These functions are all 'methods' for class 'glm' or 'summary.glm'
     objects.

_U_s_a_g_e:

     ## S3 method for class 'glm':
     summary(object, dispersion = NULL, correlation = FALSE,
            symbolic.cor = FALSE, ...)

     ## S3 method for class 'summary.glm':
     print(x, digits = max(3, getOption("digits") - 3),
           symbolic.cor = x$symbolic.cor,
           signif.stars = getOption("show.signif.stars"), ...)

_A_r_g_u_m_e_n_t_s:

  object: an object of class '"glm"', usually, a result of a call to
          'glm'.

       x: an object of class '"summary.glm"', usually, a result of a
          call to 'summary.glm'.

dispersion: the dispersion parameter for the fitting family. By default
          it is obtained from 'object'.

correlation: logical; if 'TRUE', the correlation matrix of the
          estimated parameters is returned and printed.

  digits: the number of significant digits to use when printing.

symbolic.cor: logical. If 'TRUE', print the correlations in a symbolic
          form (see 'symnum') rather than as numbers.

signif.stars: logical. If 'TRUE', "significance stars" are printed for
          each coefficient.

     ...: further arguments passed to or from other methods.

_D_e_t_a_i_l_s:

     'print.summary.glm' tries to be smart about formatting the
     coefficients, standard errors, etc. and additionally gives
     "significance stars" if 'signif.stars' is 'TRUE'.

     Aliased coefficients are omitted in the returned object but (as
     from R 1.8.0) restored by the 'print' method.

     Correlations are printed to two decimal places (or symbolically):
     to see the actual correlations print 'summary(object)$correlation'
     directly.

_V_a_l_u_e:

     'summary.glm' returns an object of class '"summary.glm"', a list
     with components

    call: the component from 'object'.

  family: the component from 'object'.

deviance: the component from 'object'.

contrasts: the component from 'object'.

df.residual: the component from 'object'.

null.deviance: the component from 'object'.

 df.null: the component from 'object'.

deviance.resid: the deviance residuals: see 'residuals.glm'.

coefficients: the matrix of coefficients, standard errors, z-values and
          p-values.  Aliased coefficients are omitted.

 aliased: named logical vector showing if the original coefficients are
          aliased.

dispersion: either the supplied argument or the estimated dispersion if
          the latter in 'NULL'

      df: a 3-vector of the rank of the model and the number of
          residual degrees of freedom, plus number of non-aliased
          coefficients.

cov.unscaled: the unscaled ('dispersion = 1') estimated covariance
          matrix of the estimated coefficients.

cov.scaled: ditto, scaled by 'dispersion'.

correlation: (only if 'correlation' is true.)  The estimated
          correlations of the estimated coefficients.

symbolic.cor: (only if 'correlation' is true.)  The value of the
          argument 'symbolic.cor'.

_S_e_e _A_l_s_o:

     'glm', 'summary'.

_E_x_a_m_p_l_e_s:

     ## --- Continuing the Example from  '?glm':

     summary(glm.D93)

