boolprof               package:boolean               R Documentation

_G_r_a_p_h _P_r_o_f_i_l_e _L_i_k_e_l_i_h_o_o_d_s _f_o_r _B_o_o_l_e_a_n _B_i_n_a_r_y _R_e_s_p_o_n_s_e _M_o_d_e_l_s

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

     This command produces a graph of the profile likelihood for a
     given coefficient following the estimation of a Boolean binary
     response model.

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

     boolprof(object, gvar = NULL, range = NULL, M = 100)

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

  object: The object created by the boolean command. 

    gvar: A character string indicating the name of variable associated
           with coefficient of interest. By default, 'boolprof' plots
          the profile likelihood for each estimated parameter. Use of
          the default is encouraged. Otherwise, be sure to specify the
          name of the variable including the "id" that is tacked onto
          the end of the variable  name so as to identify which
          instance of the variable is of  interest, even if the
          variable appears only once.

       M: The number of points to plot. The default (100) is usually
          sufficient.

   range: Range of points over which graph is plotted (e.g.,
          'range=seq(0,1,0.01)'). If NULL (the default), 'M' random
          draws from a multivariate normal distribution are plotted
          consistent  with the mode and Hessian for models estimated by
          maximum likelihood. For models estimated via bootstrapping or
          MCMC, 'M' values are sampled randomly from the output.

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

     Nothing is returned. A graph is plotted.

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

     Bear F. Braumoeller, Harvard University, bfbraum@fas.harvard.edu,
      Ben Goodrich, Harvard University, goodrich@fas.harvard.edu, and 
      Jacob Kline, Harvard University, jkline@fas.harvard.edu

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

     Braumoeller, Bear F. (2003) "Causal Complexity and the Study of
     Politics." _Political Analysis_ 11(3): 209-233.

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

     'boolean' and 'boolprep'

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

     set.seed(50)
     x1<-rnorm(1000)
     x2<-rnorm(1000)
     x3<-rnorm(1000)
     x4<-rnorm(1000)
     x5<-rnorm(1000)
     x6<-rnorm(1000)
     y<-1-(1-pnorm(-2+0.33*x1+0.66*x2+1*x3)*1-(pnorm(1+1.5*x4-0.25*x5)*pnorm(1+0.2*x6)))
     y <- y>runif(1000)
     bp <- boolprep("(a|(b&c))", y, a = ~ x1 + x2 + x3, b = ~ x4 + x5, c = ~ x6)
     answer <- boolean(bp, link = c("probit", "logit", "cloglog"))

     ## Plot profiles
     boolprof(answer)

