fwdtrMlePlot             package:Rfwdmv             R Documentation

_P_l_o_t _m_e_t_h_o_d _f_o_r _f_w_d_t_r _o_b_j_e_c_t_s

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

     Produces a plot of maximum likelihood estimates of transformation
     parameters during all steps of the forward search.

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

     fwdtrMlePlot(x, psfrag.labels = FALSE)

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

       x: a 'fwdtr' object.

psfrag.labels: a logical value.  If 'TRUE' then the x, y, and main
          labels are set to "xlab", "ylab", and "main" for replacement
          via the psfrag utility.

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

     Estimates that vary wildly are associated with variables that do
     no have to be transformed.

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

     an empty list is invisibly returned

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

     Fabrizio Laurini

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

     Atkinson, A. C., Riani, M. and Cerioli, A. (2004) Exploring
     Multivariate Data with the Forward Search. Springer-Verlag New
     York.

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

     'fwdtr.object'

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

     ## Forward search on untrasformed data 

      data(mussels.dat)
      l.mle<-fwdtr(mussels.dat)

     ## Plot maximum likelihood estimates of the trasnformation parameters
     ## in each step of the forward search 

     fwdtrMlePlot(l.mle)

      
     ## Test variables 2 and 5
     ## The forward is based on untransformed data for variables 1, 3 and 4
     ## sqrt for variable 2 and third root for variable 5

      l.mle <- fwdtr(mussels.dat, lambda = c(0.5, 1/3), col.to.transform = c(2,5))

     ## plot trajectories of maximum likelihood estimates of transformation parameters
     ## for variables 2 and 5

     fwdtrMlePlot(l.mle)

