MArrayLM-class             package:limma             R Documentation

_M_i_c_r_o_a_r_r_a_y _L_i_n_e_a_r _M_o_d_e_l _F_i_t - _c_l_a_s_s

_D_e_s_c_r_i_p_t_i_o_n:

     A list-based class for storing the results of fitting gene-wise
     linear models to a batch of microarrays. Objects are normally
     created by 'lmFit'.

_S_l_o_t_s/_C_o_m_p_o_n_e_n_t_s:

     'MArrayLM' objects do not contain any slots (apart from '.Data')
     but they should contain the following list components:

     '_c_o_e_f_f_i_c_i_e_n_t_s': 'matrix' containing fitted coefficients or
          contrasts

     '_s_t_d_e_v._u_n_s_c_a_l_e_d': 'matrix' containing unscaled standard deviations
          of the coefficients or contrasts

     '_s_i_g_m_a': 'numeric' vector containing residual standard deviations
          for each gene

     '_d_f._r_e_s_i_d_u_a_l': 'numeric' vector containing residual degrees of
          freedom for each gene

     Objects may also contain the following optional components:

     '_A_m_e_a_n': 'numeric' vector containing the average log-intensity for
          each probe over all the arrays in the original linear model
          fit. Note this vector does not change when a contrast is
          applied to the fit using 'contrasts.fit'.

     '_g_e_n_e_s': 'data.frame' containing gene names and annotation

     '_d_e_s_i_g_n': design 'matrix' of full column rank

     '_c_o_n_t_r_a_s_t_s': 'matrix' defining contrasts of coefficients for which
          results are desired

     '_F': 'numeric' vector giving moderated F-statistics for testing
          all contrasts equal to zero

     '_F._p._v_a_l_u_e': 'numeric' vector giving p-value corresponding to
          'F.stat'

     '_s_2._p_r_i_o_r': 'numeric' value giving empirical Bayes estimated prior
          value for residual variances

     '_d_f._p_r_i_o_r': 'numeric' vector giving empirical Bayes estimated
          degrees of freedom associated with 's2.prior' for each gene

     '_s_2._p_o_s_t': 'numeric' vector giving posterior residual variances

     '_t': 'matrix' containing empirical Bayes t-statistics

     '_v_a_r._p_r_i_o_r': 'numeric' vector giving empirical Bayes estimated
          prior variance for each true coefficient

     '_c_o_v._c_o_e_f_f_i_c_i_e_n_t_s': numeric 'matrix' giving the unscaled
          covariance matrix of the estimable coefficients

     '_p_i_v_o_t': 'integer' vector giving the order of coefficients in
          'cov.coefficients'. Is computed by the QR-decomposition of
          the design matrix.  

     If there are no weights and no missing values, then the 'MArrayLM'
     objects returned by 'lmFit' will also contain the QR-decomposition
     of the design matrix, and any other components returned by
     'lm.fit'.

_M_e_t_h_o_d_s:

     'RGList' objects will return dimensions and hence functions such
     as 'dim', 'nrow' and 'ncol' are defined.  'MArrayLM' objects
     inherit a 'show' method from the virtual class 'LargeDataObject'.

     The functions 'ebayes' and 'classifyTestsF' accept 'MArrayLM'
     objects as arguments.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     02.Classes gives an overview of all the classes defined by this
     package.