cdens                 package:mclust                 R Documentation

_C_o_m_p_o_n_e_n_t _D_e_n_s_i_t_y _f_o_r _P_a_r_a_m_e_t_e_r_i_z_e_d _M_V_N _M_i_x_t_u_r_e _M_o_d_e_l_s

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

     Computes component densities for observations in MVN mixture
     models parameterized by eigenvalue decomposition.

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

     cdens(modelName, data, logarithm = FALSE, parameters, warn = NULL, ...)

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

modelName: A character string indicating the model. The help file for
          'mclustModelNames' describes the available models. 

    data: A numeric vector, 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.  

logarithm: A logical value indicating whether or not the logarithm of
          the component  densities should be returned. The default is
          to return the component  densities, obtained from the log
          component densities by exponentiation. 

parameters: The parameters of the model:

          _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.  

    warn: A logical value indicating whether or not a warning should be
          issued when computations fail. The default is 'warn=FALSE'. 

     ...: Catches unused arguments in indirect or list calls via
          'do.call'. 

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

     A numeric matrix whose '[i,k]'th entry is the  density or log
     density of observation _i_ in component _k_.  The densities are
     not scaled by mixing proportions.

_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.

_N_o_t_e:

     When one or more component densities are very large in magnitude,
     it may be possible to compute the logarithm of the component
     densities but not the component densities themselves due to
     overflow.

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

     'cdensE', ..., 'cdensVVV', 'dens', 'estep', 'mclustModelNames',
     'mclustVariance', 'mclustOptions', 'do.call'

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

     z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case

     model <- me( modelName="EEE", data=faithful, z=z2)
     cdens(modelName="EEE", data=faithful, logarithm = TRUE, 
           parameters = model$parameters)[1:5,]

     odd <- seq(1, nrow(cross), by = 2)
     oddBIC <- mclustBIC(cross[odd,-1]) 
     oddModel <- mclustModel(cross[odd,-1], oddBIC) ## best parameter estimates
     names(oddModel)

     even <- odd + 1
     densities <- cdens(modelName = oddModel$modelName, data = cross[even,-1], 
                        parameters = oddModel$parameters)
     cbind(class = cross[even,1], densities)[1:5,]

