lmFit                 package:limma                 R Documentation

_L_i_n_e_a_r _M_o_d_e_l _f_o_r _S_e_r_i_e_s _o_f _A_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit linear model for each gene given a series of arrays

_U_s_a_g_e:

     lmFit(object,design=NULL,ndups=1,spacing=1,correlation=0.75,weights=NULL,method="ls",...) 

_A_r_g_u_m_e_n_t_s:

  object: object of class 'numeric', 'matrix', 'MAList', 'marrayNorm'
          or 'exprSet' containing log-ratios or log-values of
          expression for a series of microarrays

  design: the design matrix of the microarray experiment, with rows
          corresponding to arrays and columns to coefficients to be
          estimated.  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

 spacing: the spacing between duplicate spots, 'spacing=1' for
          consecutive spots

correlation: the inter-duplicate correlation

 weights: an optional numeric matrix containing weights for each spot

  method: character string, '"ls"' for least squares or '"robust"' for
          robust regression

     ...: other optional arguments to be passed to 'lm.series',
          'gls.series' or 'rlm.series'

_D_e_t_a_i_l_s:

     A linear model is fitted for each gene by calling one of
     'lm.series', 'gls.series' or 'rlm.series'. Note that the arguments
     'design', 'ndups' will be extracted from the data 'object' if
     available and do not normally need to set explicitly in the call.
     If arguments are set in the call then they will over-ride slots or
     components in the data 'object'.

_V_a_l_u_e:

     Object of class 'MArrayLM'

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

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

