mixproj                package:mclust                R Documentation

_D_i_s_p_l_a_y_s _o_n_e _s_t_a_n_d_a_r_d _d_e_v_i_a_t_i_o_n _o_f _a_n _M_V_N _m_i_x_t_u_r_e _c_l_a_s_s_i_f_i_c_a_t_i_o_n.

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

     `mixproj' displays one standard deviation of an MVN mxiture
     classification along with data in for selected pairs of
     coordinates. Function `mvn2plot' is used to plot the confidence
     ellipses.

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

     mixproj(data, ms, partition, scale = F, k = 15, ...)
     mvn2plot(mu, sigma, k)

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

    data: a matrix of onservations. 

      ms: The result of an `mstep' calculation (a list consisting of
          `mu' and `sigma'). 

partition: A integer vector giving an initial classification for each
          observation. 

  dimens: A vector of length two giving the two variables of the data
          to be plotted. 

   scale: A logical variable telling whether or not the same scale
          should be used for both variables so as to preserve geometry.
          The default does not use the same  scale. 

       k: Number of subdivisions for plotting segments of ellipsoids.
          Default: 8. 

     ...: Further arguments to plot. 

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

     `mstep', `clpairs'

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

     data(iris)
     cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3)
     z <- me( iris[,1:4], modelid = "VVV", ctoz(cl))
     pars <- mstep(iris[,1:4], modelid="VVV", z)
     mixproj(iris[,1:4], ms=pars, partition=ztoc(z), dimens=c(1,2))

