mvn                  package:mclust                  R Documentation

_U_n_i_v_a_r_i_a_t_e _o_r _M_u_l_t_i_v_a_r_i_a_t_e _N_o_r_m_a_l _F_i_t

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

     Computes the mean, covariance, and loglikelihood from fitting a
     single Gaussian to given data (univariate or multivariate normal).

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

     mvn( modelName, data, prior = NULL, warn = NULL, ...)

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

modelName: A character string representing a model name. This can be
          either '"Spherical"', '"Diagonal"', or '"Ellipsoidal"' or 
          else 
           "X" for one-dimensional data,
           "XII" for a spherical Gaussian, 
           "XXI" for a diagonal Gaussian 
           "XXX" for a general ellipsoidal Gaussian  

    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.  

   prior: Specification of a conjugate prior on the means and
          variances. The default assumes no prior. 

    warn: A logical value indicating whether or not a warning should be
          issued whenever a singularity is encountered. The default is
          set in '.Mclust\$warn'. 

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

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

     A list including the following components: 

modelName: A character string identifying the model (same as the input
          argument). 

          _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.

  loglik: The log likelihood for the data in the mixture model. 

             *  '"WARNING"' An appropriate warning if problems are
                encountered in the computations.

_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:

     'mvnX', 'mvnXII', 'mvnXXI', 'mvnXXX', 'mclustModelNames'

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

     n <- 1000

     set.seed(0)
     x <- rnorm(n, mean = -1, sd = 2)
     mvn(modelName = "X", x) 

     mu <- c(-1, 0, 1)

     set.seed(0)
     x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), 
                MARGIN = 2, STATS = mu, FUN = "+")
     mvn(modelName = "XII", x) 
     mvn(modelName = "Spherical", x) 

     set.seed(0)
     x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), 
                MARGIN = 2, STATS = mu, FUN = "+")
     mvn(modelName = "XXI", x)
     mvn(modelName = "Diagonal", x)

     Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3)
     set.seed(0)
     x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), 
                MARGIN = 2, STATS = mu, FUN = "+")
     mvn(modelName = "XXX", x) 
     mvn(modelName = "Ellipsoidal", x) 

