netlogit                 package:sna                 R Documentation

_L_o_g_i_s_t_i_c _R_e_g_r_e_s_s_i_o_n _f_o_r _N_e_t_w_o_r_k _D_a_t_a

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

     `netlogit' performs a logistic regression of the network variable
     in `y' on the network variables in stack `x'.  The resulting fits
     (and coefficients) are then tested against the indicated null
     hypothesis.

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

     netlogit(y, x, mode="digraph", diag=FALSE, nullhyp="cugtie", 
         reps=1000)

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

       y: Dependent network variable.  This should be a matrix, for
          obvious reasons; `NA's are allowed, and the data should be
          dichotomous. 

       x: Data array containing the stack of independent network
          variables.  By assumption, the first dimension of the array
          indexes the graph, with the next two indexing the actors. 
          Note that `NA's are permitted, as is dichotomous data. 

    mode: String indicating the type of graph being evaluated. 
          "Digraph" indicates that edges should be interpreted as
          directed; "graph" indicates that edges are undirected. 
          `mode' is set to "digraph" by default. 

    diag: Boolean indicating whether or not the diagonal should be
          treated as valid data.  Set this true if and only if the data
          can contain loops.  `diag' is `FALSE' by default. 

 nullhyp: String indicating the particular null hypothesis against
          which to test the observed estimands.  A value of "cug"
          implies a conditional uniform graph test (see `cugtest')
          controlling for order only; "cugden" controls for both order
          and tie probability; "cugtie" controls for order and tie
          distribution (via bootstrap); and "qap" implies that the QAP
          null hypothesis (see `qaptest') should be used. 

    reps: Integer indicating the number of draws to use for quantile
          estimation.  (Relevant to the null hypothesis test only - the
          analysis itself is unaffected by this parameter.)  Note that,
          as for all Monte Carlo procedures, convergence is slower for
          more extreme quantiles.  By default, `reps'=1000. 

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

     `netlogit' is primarily a front-end to the built-in `glm' routine.
      `netlogit' handles vectorization, sets up `glm' options, and
     deals with null hypothesis testing; the actual fitting is taken
     care of by `glm'.  

     Logistic network regression using is directly analogous to
     standard logistic regression elementwise on the appropriately
     vectorized adjacency matrices of the networks involved.  As such,
     it is often a more appropriate model for fitting dichotomous
     response networks than is linear network regression.  

     Null hypothesis tests for logistic network regression are handled
     using either the conditional uniform graph hypothesis (the
     default) or QAP.  See the help pages for these tests for a fuller
     description of each.  Reasonable printing and summarizing of
     `netlogit' objects is provided by `print.netlogit' and
     `summary.netlogit', respectively.  No plot methods exist at this
     time.

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

     An object of class `netlogit'

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

     Carter T. Butts ctb@andrew.cmu.edu

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

     Butts, C.T., and Carley, K.M.  (2001).  ``Multivariate Methods for
     Interstructural Analysis.''  CASOS working paper, Carnegie Mellon
     University.

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

     `glm', `netlm'

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

     #Create some input graphs
     x<-rgraph(20,4)

     #Create a response structure
     y.l<-x[1,,]+4*x[2,,]+2*x[3,,]   #Note that the fourth graph is 
                                     #unrelated
     y.p<-apply(y.l,c(1,2),function(a){1/(1+exp(-a))})
     y<-rgraph(20,tprob=y.p)

     #Fit a netlogit model
     nl<-netlogit(y,x,reps=100)

     #Examine the results
     summary(nl)

