relrelimp               package:relimp               R Documentation

_C_o_m_p_a_r_i_s_o_n _o_f _R_e_l_a_t_i_v_e _I_m_p_o_r_t_a_n_c_e_s _i_n _a 
_M_u_l_t_i_n_o_m_i_a_l _L_o_g_i_t _M_o_d_e_l

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

     Produces a summary of the relative importance of two predictors or
     two sets of predictors in a fitted 'multinom' model object, and
     compares  relative importances across two of the fitted logit
     models.

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

     relrelimp(object, set1=NULL, set2=NULL, label1="set1", label2="set2", 
               subset=TRUE, 
               response.cat1=NULL, response.cat2=NULL)

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

  object: A model object of class 'multinom'

    set1: An index or vector of indices for the effects to be included
          in the numerator of the comparison

    set2: An index or vector of indices for the effects to be included
          in the denominator of the comparison

  label1: A character string; mnemonic name for the  variables in
          'set1'

  label2: A character string; mnemonic name for the variables in 'set2'

  subset: Either a vector of numeric indices for the cases to be
          included in the standardization of effects, or a vector of
          logicals ('TRUE' for inclusion) whose length is the same as
          the number of rows in the model frame, 'object$model'. The
          default choice is to include all cases in the model frame.

response.cat1: A character string used to specify the first regression
          of interest (i.e., the regression which predicts the log odds
          on 'response.cat1' versus the model's  reference category). 
          The 'response.cat1' argument should be an element of
          'object$lab'.

response.cat2: A character string used to specify the second regression
          of interest (i.e., the regression which predicts the log odds
          on 'response.cat2' versus the model's  reference category). 
          The 'response.cat2' argument should be an element of
          'object$lab'.

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

     Computes a relative importance summary as described in 'relimp',
     for each of the two regressions specified by  'response.cat1' and
     'response.cat2' (relative to the same reference category); and
     computes the  difference of those two relative importance
     summaries, along with an estimated standard error for that
     difference.

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

     An object of class 'relrelimp', with at least the following
     components: 

   model: The call used to construct the model object summarized

    sets: The two sets of indices specified as arguments

response.category: A character vector containing the specified
          'response.cat1' and 'response.cat2'

log.ratio: The natural logarithm of the ratio of effect standard
          deviations corresponding to the two sets specified. A vector
          with  three components: the first is for 'response.cat1'
          versus the reference category, the second for 'response.cat2'
          versus the reference category, the third is the difference.

se.log.ratio: Estimated standard errors for the elements of 
          'log.ratio'

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

     David Firth, d.firth@warwick.ac.uk

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

     'relimp'

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

     ##  Data on housing and satisfaction, from Venables and Ripley
     library(MASS)
     library(nnet)
     data(housing)
     house.mult <- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
       data = housing)
     relrelimp(house.mult, set1 = 2:3, set2 = 7, 
                           label1 = "Influence", label2 = "Contact",
                           response.cat1 = "Medium", response.cat2 = "High")
     ## Computes the relative contribution of Influence and Contact in 
     ## each of the two logistic regressions (Med/Low and High/Low), and
     ## compares those two relative-contribution measures.

