mvnX                 package:mclust                 R Documentation

_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 MVN or Gaussian.

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

     mvnX(data)
     mvnXII(data)
     mvnXXI(data)
     mvnXXX(data)

_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. 

_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 of including the parameters of the Gaussian model best
     fitting the data, and the corresponding loglikelihood for the data
     under the model.

_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:

     '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) 

