vif                   package:car                   R Documentation

_V_a_r_i_a_n_c_e _I_n_f_l_a_t_i_o_n _F_a_c_t_o_r_s

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

     Calculates variance-inflation and generalized variance-inflation
     factors for linear and generalized linear models.

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

     vif(mod)

     ## S3 method for class 'lm':
     vif(mod)

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

     mod: an object that inherits from class 'lm', such as an 'lm' or
          'glm' object.

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

     If all terms in an unweighted linear model have 1 df, then the
     usual variance-inflation factors are calculated.

     If any terms in an unweighted linear model have more than 1 df,
     then generalized variance-inflation factors (Fox and Monette,
     1992) are calculated. These are interpretable as the inflation in
     size of the confidence ellipse or ellipsoid for the coefficients
     of the term in comparison with what would be obtained for
     orthogonal data. 

     The generalized vifs are invariant with respect to the coding of
     the terms in the model (as long as the subspace of the columns of
     the model matrix pertaining to each term is invariant). To adjust
     for the dimension of the confidence ellipsoid, the function also
     prints GVIF^{1/(2times df)}.

     Through a further generalization, the implementation here is
     applicable as well to other sorts of models, in particular
     weighted linear models and  generalized linear models, that
     inherit from class 'lm'.

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

     A vector of vifs, or a matrix containing one row for each term in
     the model, and columns for the GVIF, df, and GVIF^{1/(2times df)}.

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

     Henric Nilsson and John Fox jfox@mcmaster.ca

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

     Fox, J. and Monette, G. (1992) Generalized collinearity
     diagnostics. _JASA_, *87*, 178-183.

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

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

     vif(lm(prestige~income+education, data=Duncan))
     ##    income education 
     ##  2.104900  2.104900 
     vif(lm(prestige~income+education+type, data=Duncan))
     ##               GVIF Df GVIF^(1/2Df)
     ## income    2.209178  1     1.486330
     ## education 5.297584  1     2.301648
     ## type      5.098592  2     1.502666

