estimateSigma              package:plw              R Documentation

_F_i_t _z_e_r_o _m_e_a_n _m_u_l_t_i_v_a_r_i_a_t_e _t-_d_i_s_t_r_i_b_u_t_i_o_n, _k_n_o_w_n _d_f

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

     Estimate the covariance matrix Sigma of the multivariate
     t-distribution with zero expectation assuming the degrees of
     freedom is known.

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

     estimateSigma(y, m, v, maxIter = 100, epsilon = 1e-06, verbose = FALSE)

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

       y: data matrix

       m: degrees of freedom

       v: scale parameter

 maxIter: maximum number of iterations

 epsilon: convergence criteria

 verbose: print computation info or not

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

     The multivariate t-distribution is parametrized as:

                         y|c ~ N(mu,c*Sigma)


                       c ~ InvGamma(m/2,m*v/2)

     Here N denotes a multivariate normal distribution,  Sigma is a
     covariance matrix and  InvGamma(a,b) is the inverse-gamma
     distribution with density function

                f(x)=b^a exp{-b/x} x^{-a-1} /Gamma(a)

     In this application mu equals zero, and m is the degrees of
     freedom.

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

   Sigma: Estimated covariance matrix for y

    iter: Number of iterations

_A_u_t_h_o_r(_s):

     Magnus Astrand

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

     Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements
     of Statistical Learning, volume 1. Springer, first edition.

     Kristiansson, E., Sjogren, A., Rudemo, M., Nerman, O. (2005).
     Weighted Analysis of Paired Microarray Experiments. Statistical
     Applications in Genetics and Molecular Biology 4(1)

     Astrand, M. et al. (2007a). Improved covariance matrix estimators
     for weighted analysis of microarray data. Journal of Computational
     Biology, Accepted.

     Astrand, M. et al. (2007b). Empirical Bayes models for
     multiple-probe type arrays at the probe level. Bioinformatics,
     Submitted 1 October 2007.

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

     estimateSigmaMV

