mlest                 package:mvnmle                 R Documentation

_M_L _E_s_t_i_m_a_t_i_o_n _o_f _M_u_l_t_i_v_a_r_i_a_t_e _N_o_r_m_a_l _D_a_t_a

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

     Finds the maximum likelihood estimates of the mean vector and
     variance-covariance matrix for multivariate normal data with
     (potentially) missing values.

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

     mlest(data, ...)

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

    data: A data frame or matrix containing multivariate normal data. 
          Each row should correspond to an observation, and each column
          to a component of the multivariate vector.  Missing values
          should be coded by 'NA'.

     ...: Optional arguments to be passed to the nlm optimization
          routine.

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

     The estimate of the variance-covariance matrix returned by `mlest'
     is necessarily positive semi-definite.  Internally, `nlm' is used
     to minimize the negative log-likelihood, so optional arguments
     mayh be passed to `nlm' which modify the details of the
     minimization algorithm, such as `iterlim'.  The likelihood is
     specified in terms of the inverse of the Cholesky factor of the
     variance-covariance matrix (see Pinheiro and Bates 2000).

     `mlest' cannot handle data matrices with more than 50 variables.
     Each varaible must also be observed at least once.

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

   muhat: MLE of the mean vector.

sigmahat: MLE of the variance-covariance matrix.

   value: The objective function that is minimized by `nlm'. Is is
          proportional to twice the negative log-likelihood.

gradient: The curvature of the likelihood surface at the MLE, in the
          parameterization used internally by the optimization
          algorithm.  This parameterization is: mean vector first,
          followed by the log of the diagonal elements of the inverse
          of the Cholesky factor, and then the elements of the inverse
          of the Cholesky factor above the main diagonal.  These
          off-diagonal elements are ordered by column (left to right),
          and then by row within column (top to bottom).

stop.code: The stop code returned by `nlm'.

iterations: The number of iterations used by `nlm'.

_R_e_f_e_r_e_n_c_e_s:

     Little, R. J. A., and Rubin, D. B. (1987) Statistical Analysis
     with Missing Data.  New York: Wiley.

     Pinheiro, J. C., and Bates, D. M.  (1996) Unconstrained
     parametrizations for variance-covariance matrices. Statistics and
     Computing 6, 289-296.

     Pinheiro, J. C., and Bates, D. M.  (2000) Mixed-effects models in
     S and S-PLUS.  New York: Springer.

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

     `nlm'

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

     library(mvnmle)

     data(apple)
     mlest(apple)

     data(missvals)
     mlest(missvals, iterlim=400)

