f.Q                 package:GeneMeta                 R Documentation

_C_o_m_p_u_t_e _C_o_c_h_r_a_n'_s _Q _s_t_a_t_i_s_t_i_c

_D_e_s_c_r_i_p_t_i_o_n:

     Compute Cochran's Q statistic for testing whether the a fixed
     effects or a random effects model will be appropriate.

_U_s_a_g_e:

     f.Q(dadj, varadj)

_A_r_g_u_m_e_n_t_s:

    dadj: A matrix, each row is a gene, each column a study, of the
          estimated t-statistics. 

  varadj: A matrix, each row is a gene, each column a study, of the
          estimated, adjusted variances of the t-statistics.

_D_e_t_a_i_l_s:

     A straightforward computation of Cochran's Q statistic. If the
     null hypothesis that the data are well modeled by a fixed effects
     design is true then the estimate Q values will have approximately
     a chi-squared distribution with degrees of freedom equal to the
     number of studies minus one.

_V_a_l_u_e:

     A vector of length equal to the number of rows of 'dadj' with the
     Q statistics.

_A_u_t_h_o_r(_s):

     L. Lusa and R. Gentleman

_R_e_f_e_r_e_n_c_e_s:

     Choi et al, Combining multiple microarray studies and modeling
     interstudy variation. Bioinformatics, 2003, i84-i90.

_S_e_e _A_l_s_o:

     'dstar','sigmad'

_E_x_a_m_p_l_e_s:

     ##none now, this requires a pretty elaborate example

