colldiag               package:perturb               R Documentation

_C_o_n_d_i_t_i_o_n _i_n_d_e_x_e_s _a_n_d _v_a_r_i_a_n_c_e _d_e_c_o_m_p_o_s_i_t_i_o_n _p_r_o_p_o_r_t_i_o_n_s

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

     Calculates condition indexes and variance decomposition
     proportions in order to test for collinearity among the
     independent variables of a regression model and identifies the
     sources of collinearity if present

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

     colldiag(mod, scale = TRUE, center = FALSE, add.intercept = TRUE)

     ## S3 method for class 'colldiag':
     print(x,dec.places=3,fuzz=NULL,fuzzchar=".",...)

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

     mod: A model object or data-frame

   scale: If FALSE, the data are left unscaled. Default is TRUE

  center: If TRUE, data are centered. Default is FALSE

add.intercept: if TRUE, an intercept is added. Default is TRUE

       x: A 'colldiag' object

dec.places: number of decimal places to use when printing

    fuzz: variance decomposition proportions less than _fuzz_ are
          printed as _fuzzchar_

fuzzchar: character for small variance decomposition proportion values

     ...: arguments to be passed on to or from other methods

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

     'Colldiag' is an implementation of the regression collinearity
     diagnostic procedures found in Belsley, Kuh, and Welsch (1980).
     These procedures examine the conditioning of the matrix of
     independent variables.

     'Colldiag' computes the condition indexes of the matrix. If the
     largest condition index (the condition number) is _large_ (Belsley
     et al suggest 30 or higher), then there may be collinearity
     problems. All _large_ condition indexes may be worth
     investigating.

     'Colldiag' also provides further information that may help to
     identify the source of these problems, the _variance decomposition
     proportions_ associated with each condition index.  If a large
     condition index is associated two or more variables with _large_
     variance decomposition proportions, these variables may be causing
     collinearity problems.  Belsley et al suggest that a _large_
     proportion is 50 percent or more.

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

     A colldiag object 

condindx: A vector of condition indexes

      pi: A matrix of variance decomposition proportions


     'print.colldiag' prints the condition indexes as the first column
     of a table with the variance decomposition proportions beside
     them. 'print.colldiag' has a 'fuzz' option to suppress printing of
     small numbers. If fuzz is used, small values are replaces by a
     period .. 'Fuzzchar' can be used to specify an alternative
     character.

_N_o_t_e:

     Colldiag is based on the Stata program 'coldiag' by Joseph
     Harkness joe.harkness@jhu.edu, Johns Hopkins University.

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

     John Hendrickx John_Hendrickx@yahoo.com

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

     D. Belsley, E. Kuh, and R. Welsch (1980). Regression Diagnostics.
     Wiley.

     Belsley, D.A. (1991). Conditioning diagnostics, collinearity and
     weak data in regression. New York: John Wiley & Sons.

_S_e_e _A_l_s_o:

     'lm', 'scale', 'svd', '[car]''vif', '[Design]''vif', 'perturb'

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

     # Belsley (1991). "Conditioning Diagnostics"
     # The Consumption Function (pp. 149-154)
     data(consumption)

     ct1<-c(NA,c[-length(c)])

     # compare (5.3)
     m1<-lm(c~ct1+dpi+r+d_dpi)
     anova(m1)
     summary(m1)

     # compare exhibit 5.11
     cor(cbind(ct1,dpi,r,d_dpi),use="complete.obs")

     # compare exhibit 5.12
     cd<-colldiag(m1)
     cd
     print(cd,fuzz=.3)

