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,block=NULL,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: positive integer giving the number of times each gene is
          printed on an array

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

   block: vector or factor specifying a blocking variable

correlation: the inter-duplicate or inter-technical replicate
          correlation

 weights: 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', 'spacing' and 'weights' 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.

