mclust1Dplot             package:mclust             R Documentation

_P_l_o_t _o_n_e-_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:

     Plot one-dimensional data given parameters of an MVN mixture model
      for the data.

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

     mclust1Dplot(data, parameters=NULL, z=NULL, 
                  classification=NULL, truth=NULL, uncertainty=NULL, 
                  what = c("classification", "density", "errors", "uncertainty"),
                  symbols=NULL, ngrid=length(data), xlab = NULL, xlim=NULL, CEX=1, 
                  identify=FALSE, ...)

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

    data: A numeric vector of observations. Categorical variables are
          not allowed. 

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:

          _p_r_o The vector of mixing proportions.

          _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), '"density"', '"errors"',
          '"uncertainty"'. 

 symbols: Either an integer or character vector assigning a plotting
          symbol to each unique class 'classification'. Elements in
          'symbols' correspond to classes in 'classification' in order
          of appearance in the observations (the order used by the 
          function 'unique'). The default is to use a single plotting
          symbol _|_. Classes are delineated by showing them in
          separate lines above the whole of the data. 

   ngrid: Number of grid points to use for density computation over the
          interval spanned by the data. The default is the length of
          the data set. 

    xlab: An argument specifying a label for the horizontal axis. 

    xlim: An argument specifying bounds 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. 

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 location of the mixture components, classification,
     uncertainty, density and/or classification errors. Points in the
     different classes are shown in separated levels above the whole of
     the data.

_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:

     'mclust2Dplot', 'clPairs', 'coordProj'

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

     n <- 250 ## create artificial data
     set.seed(1)
     y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
     yclass <- c(rep(1,n), rep(2,n), rep(3,n))

     yModel <- mclustModel(y, mclustBIC(y))

     mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
                  what = "classification", identify = TRUE)

     mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
                  truth = yclass, what = "errors", identify = TRUE)

     mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, 
                  what = "density", identify = TRUE)

     mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters,
                 what = "uncertainty", identify = TRUE)

