mvnX                 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 (univariate or multivariate normal).

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

     mvnX(data, prior = NULL, warn = NULL, ...)
     mvnXII(data, prior = NULL, warn = NULL, ...)
     mvnXXI(data, prior = NULL, warn = NULL, ...)
     mvnXXX(data, prior = NULL, warn = NULL, ...)

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

    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'. 

_D_e_t_a_i_l_s:

        *  'mvnXII'  computes the best fitting Gaussian with the
           covariance  restricted to be a multiple of the identity. 

        *  'mvnXXI'  computes the best fitting Gaussian with the
           covariance  restricted to be diagonal. 

        *  'mvnXXX'  computes the best fitting Gaussian with
           ellipsoidal  (unrestricted) covariance. 

_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. 

        "_W_A_R_N_I_N_G" 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: An R Package
     for Normal Mixture Modeling and Model-Based Clustering, Technical
     Report, Department of Statistics, University of Washington.

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

     'mvn', 'mstepE'

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

     n <- 1000

     set.seed(0)
     x <- rnorm(n, mean = -1, sd = 2)
     mvnX(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 = "+")
     mvnXII(x) 

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

