coordProj               package:mclust               R Documentation

_C_o_o_r_d_i_n_a_t_e _p_r_o_j_e_c_t_i_o_n_s _o_f _m_u_l_t_i_d_i_m_e_n_s_i_o_n_a_l _d_a_t_a _m_o_d_e_l_e_d _b_y _a_n _M_V_N _m_i_x_t_u_r_e.

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

     Plots coordinate projections given multidimensional data and
     parameters of an MVN mixture model for the data.

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

     coordProj(data, dimens=c(1,2), parameters=NULL, z=NULL,
               classification=NULL, truth=NULL, uncertainty=NULL, 
               what = c("classification", "errors", "uncertainty"),
               quantiles = c(0.75, 0.95), symbols=NULL, colors=NULL, scale = FALSE, 
               xlim=NULL, ylim=NULL, CEX = 1, PCH = ".", identify = FALSE, ...)

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

    data: A numeric matrix or data frame of observations. Categorical
          variables are not allowed. If a matrix or data frame, rows
          correspond to observations and columns correspond to
          variables. 

  dimens: A vector of length 2 giving the integer dimensions of the
          desired coordinate projections. The default is 'c(1,2)', in
          which the first dimension is plotted against the second. 

parameters: A named list giving the parameters of an _MCLUST_ model, 
          used to produce superimposing ellipses on the plot.  The
          relevant components are as follows:

          _m_e_a_n The mean for each component. If there is more than one
               component, this is a matrix whose kth column is the mean
               of the _k_th component of the mixture model.

          _v_a_r_i_a_n_c_e A list of variance parameters for the model. The
               components of this list depend on the model
               specification. See the help file for 'mclustVariance'
               for details.

       z: A matrix in which the '[i,k]'th entry gives the probability
          of observation _i_ belonging to the _k_th class.  Used to
          compute 'classification' and 'uncertainty' if those arguments
          aren't available. 

classification: A numeric or character vector representing a
          classification of observations (rows) of 'data'. If present
          argument 'z' will be ignored. 

   truth: A numeric or character vector giving a known classification
          of each data point. If 'classification' or 'z' is also
          present,  this is used for displaying classification errors. 

uncertainty: A numeric vector of values in _(0,1)_ giving the
          uncertainty of each data point. If present argument 'z' will
          be ignored. 

    what: Choose from one of the following three options:
          '"classification"' (default), '"errors"', '"uncertainty"'.  

quantiles: A vector of length 2 giving quantiles used in plotting
          uncertainty. The smallest symbols correspond to the smallest
          quantile (lowest uncertainty), medium-sized (open) symbols to
          points falling between the given quantiles, and large
          (filled) symbols to those in the largest quantile (highest
          uncertainty). The default is _(0.75,0.95)_.  

 symbols: Either an integer or character vector assigning a plotting
          symbol to each unique class in 'classification'. Elements in
          'colors' correspond to classes in order of appearance in the
          sequence of observations (the order used by the function
          'unique').  The default is given is
          '.Mclust\$classPlotSymbols'. 

  colors: Either an integer or character vector assigning a color to
          each unique class in 'classification'. Elements in 'colors'
          correspond to classes in order of appearance in the sequence
          of observations (the order used by the function 'unique'). 
          The default is given is '.Mclust\$classPlotColors'. 

   scale: A logical variable indicating whether or not the two chosen
          dimensions should be plotted on the same scale, and thus
          preserve the shape of the distribution. Default:
          'scale=FALSE'  

xlim, ylim: Arguments specifying bounds for the ordinate, abscissa of
          the plot. This may be useful for when comparing plots. 

     CEX: An argument specifying the size of the plotting symbols.  The
          default value is 1. 

     PCH: An argument specifying the symbol to be used when a
          classificatiion has not been specified for the data. The
          default value is a small dot ".". 

identify: A logical variable indicating whether or not to add a title
          to the plot identifying the dimensions used. 

     ...: Other graphics parameters. 

_S_i_d_e _E_f_f_e_c_t_s:

     A plot showing a two-dimensional coordinate projection of the
     data, together with the location of the  mixture components,
     classification, uncertainty, and/or classification errors.

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

     C. Fraley and A. E. Raftery (2002). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_. 

     C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal
     Mixture Modeling and Model-Based Clustering,  Technical Report no.
     504, Department of Statistics, University of Washington.

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

     'clPairs', 'randProj', 'mclust2Dplot', 'mclustOptions'

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

     est <- meVVV(iris[,-5], unmap(iris[,5]))

     ## Not run: 
     par(pty = "s", mfrow = c(1,1))
     coordProj(iris[,-5], dimens=c(2,3), parameters = msEst$parameters, z = est$z,
               what = "classification", identify = TRUE) 
     coordProj(iris[,-5], dimens=c(2,3), parameters = msEst$parameters, z = est$z,
               truth = iris[,5], what = "errors", identify = TRUE) 
     coordProj(iris[,-5], dimens=c(2,3), parameters = msEst$parameters, z = est$z,
               what = "uncertainty", identify = TRUE) 
     ## End(Not run)

