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 models.

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


     vif(mod)

     vif.lm(mod)

     vif.default(mod)

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

     mod: an unweighted `lm' object.

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

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

     If any terms 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)}.

     Currently, `vif' is only defined for linear models; `vif.default'
     is
 a dummy function that generates an error.

_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):

     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:


     data(Duncan)
     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

