uncerPlot               package:mclust               R Documentation

_U_n_c_e_r_t_a_i_n_t_y _P_l_o_t _f_o_r _M_o_d_e_l-_B_a_s_e_d _C_l_u_s_t_e_r_i_n_g

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

     Plots the uncertainty in converting a conditional probablility
     from EM to a classification in model-based clustering.

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

     uncerPlot(z, truth, ...)

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

       z: A matrix whose _[i,k]_th entry is the conditional probability
          of the ith observation belonging to the _k_th component of
          the mixture.   

   truth: A numeric or character vector giving the true classification
          of the data.  

    ... : Provided to allow lists with elements other than the
          arguments can be passed in indirect or list calls with
          'do.call'. 

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

     When 'truth' is provided and the number of classes is compatible
     with 'z', the function 'compareClass' is used to to find best
     correspondence between classes in 'truth' and 'z'.

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

     A plot of the uncertainty profile of the data, with uncertainties
     in increasing order of magnitude. If 'truth' is supplied and the
     number of classes is the same as the number of columns of  'z',
     the uncertainty of the misclassified data is marked by vertical
     lines on the plot.

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

     C. Fraley and A. E. Raftery (2002a). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_. See <URL:
     http://www.stat.washington.edu/mclust>.

     C. Fraley and A. E. Raftery (2002b). MCLUST: Software for
     model-based clustering, density estimation and  discriminant
     analysis.  Technical Report, Department of Statistics, University
     of Washington.  See <URL: http://www.stat.washington.edu/mclust>.

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

     'EMclust', 'em', 'me', 'mapClass'

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

     data(iris)
     irisMatrix <- as.matrix(iris[,1:4])

     irisBic <- EMclust(irisMatrix)
     irisSumry3 <-  summary(irisBic, irisMatrix, G = 3)

     uncerPlot(z = irisSumry3$z)
      
     uncerPlot(z = irisSumry3$z, truth = rep(1:3, rep(50,3)))

     do.call("uncerPlot", c(irisSumry3, list(truth = rep(1:3, rep(50,3)))))

