dens                 package:mclust                 R Documentation

_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_s

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

     Computes densities of obseravations in parameterized MVN mixtures.

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

     dens(modelName, data, mu, logarithm, ...)

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

modelName: A character string indicating the model. Possible models: 

           "E" for spherical, equal variance (one-dimensional) 
           "V" for spherical, variable variance (one-dimensional) 

           "EII": spherical, equal volume 
           "VII": spherical, unequal volume 
           "EEI": diagonal, equal volume, equal shape 
           "VEI": diagonal, varying volume, equal shape 
           "EVI": diagonal, equal volume, varying shape 
           "VVI": diagonal, varying volume, varying shape 
           "EEE": ellipsoidal, equal volume, shape, and orientation
            "EEV": ellipsoidal, equal volume and equal shape
           "VEV": ellipsoidal, equal shape 
           "VVV": ellipsoidal, varying volume, shape, and orientation 

           For fitting a single Gaussian,

           "X": one-dimensional 
           "XII": spherical 
           "XXI": diagonal 
           "XXX": ellipsoidal 

    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.  

      mu: The mean for each component. If there is more than one
          component, 'mu' is a matrix whose columns are the means of
          the components. 

logarithm: Return logarithm of the density, rather than the density
          itself. Default: FALSE

     ...: Other arguments, such as an argument describing the variance.
          See 'cdens'. 

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

     A numeric vector whose _i_th component is the density of
     observation  _i_ in the MVN mixture specified by 'mu' and '...'.

_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:

     'grid1', 'cdens', 'mclustOptions', 'do.call'

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

     n <- 100 ## create artificial data

     set.seed(0)
     x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
                matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
     xclass <- c(rep(1,n),rep(2,n))
     clPairs(x, cl = xclass, sym = c("1","2")) ## display the data

     set.seed(0)
     I <- sample(1:(2*n))
     x <- x[I, ]
     xclass <- xclass[I]

     odd <- seq(1, 2*n, by = 2)
     oddBic <- EMclust(x[odd, ]) 
     oddSumry <- summary(oddBic, x[odd, ]) ## best parameter estimates
     names(oddSumry)

     oddDens <- dens(modelName = oddSumry$modelName, data = x,
        mu = oddSumry$mu, decomp = oddSumry$decomp, pro = oddSumry$pro)

     ## Not run: 
     oddDens <- do.call("dens", c(list(data = x), oddSumry))  ## alternative call
     ## End(Not run)

     even <- odd + 1
     evenBic <- EMclust(x[even, ]) 
     evenSumry <- summary(evenBic, x[even, ]) ## best parameter estimates
     evenDens <- do.call( "dens", c(list(data = x), evenSumry))

     cbind(class = xclass, odd = oddDens, even = evenDens)

