mrlm                  package:limma                  R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l _t_o _M_i_c_r_o_r_r_a_y _D_a_t_a _b_y _R_o_b_u_s_t _R_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a linear model genewise to expression data from a series of
     arrays. The fit is by robust M-estimation allowing for a small
     proportion of outliers. This is a utility function for 'lmFit'.

_U_s_a_g_e:

     mrlm(M,design=NULL,ndups=1,spacing=1,weights=NULL,...)

_A_r_g_u_m_e_n_t_s:

       M: numeric matrix containing log-ratio or log-expression values
          for a series of microarrays, rows correspond to genes and
          columns to arrays.

  design: numeric design matrix defining the linear model, with rows
          corresponding to arrays and columns to comparisons to be
          estimated. The number of rows must match the number of
          columns of 'M'. Defaults to the unit vector meaning that the
          arrays are treated as replicates.

   ndups: a positive integer giving the number of times each gene is
          printed on an array. 'nrow(M)' must be divisible by 'ndups'.

 spacing: the spacing between the rows of 'M' corresponding to
          duplicate spots, 'spacing=1' for consecutive spots.

 weights: numeric matrix of the same dimension as 'M' containing
          weights. If it is of different dimension to 'M', it will be
          filled out to the same size. 'NULL' is equivalent to equal
          weights.

     ...: any other arguments are passed to 'rlm.default'.

_D_e_t_a_i_l_s:

     This is a utility function used by the higher level function
     'lmFit'. Most users should not use this function directly but
     should use 'lmFit' instead.

     This function fits a linear model for each gene by calling the
     function 'rlm' from the MASS library.

     Warning: don't use weights with this function unless you
     understand how 'rlm' treats weights. The treatment of weights is
     somewhat different from that of 'lm.series' and 'gls.series'.

_V_a_l_u_e:

     A list with components 

coefficients: numeric matrix containing the estimated coefficients for
          each linear model. Same number of rows as 'M', same number of
          columns as 'design'.

stdev.unscaled: numeric matrix conformal with 'coef' containing the
          unscaled standard deviations for the coefficient estimators.
          The standard errors are given by 'stdev.unscaled * sigma'.

   sigma: numeric vector containing the residual standard deviation for
          each gene.

df.residual: numeric vector giving the degrees of freedom corresponding
          to 'sigma'.

      qr: QR decomposition of 'design'.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'rlm'.

     An overview of linear model functions in limma is given by
     06.LinearModels.