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 family used. Either a
          single numerical value or 'NULL' (the default), when it is
          inferred from 'object' (see Details).

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'. The
     'coefficients' component of the result gives the estimated
     coefficients and their estimated standard errors, together with
     their ratio.  This third column is labelled 't ratio' if the
     dispersion is estimated, and 'z ratio' if the dispersion is known
     (or fixed by the family).  A fourth column gives the two-tailed
     p-value corresponding to the t or z ratio based on a Student t or
     Normal reference distribution.  (It is possible that the
     dispersion is not known and there are no residual degrees of
     freedom from which to estimate it.  In that case the estimate is
     'NaN'.)

     Aliased coefficients are omitted in the returned object but
     restored by the 'print' method.

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

     The dispersion of a GLM is not used in the fitting process, but it
     is needed to find standard errors. If 'dispersion' is not supplied
     or 'NULL', the dispersion is taken as '1' for the 'binomial' and
     'Poisson' families, and otherwise estimated by the residual
     Chisquared statistic (calculated from cases with non-zero weights)
     divided by the residual degrees of freedom.

     'summary' can be used with Gaussian 'glm' fits to handle the case
     of a linear regression with known error variance, something not
     handled by 'summary.lm'.

_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 inferred/estimated
          dispersion if the latter is '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)

