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',
          'exprSet' or 'PLMset' 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 on the
          arrays. Has length equal to the number of arrays.

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:

     This function accepts data from a experiment involving a series of
     microarrays with the same set of probes. A linear model is fitted
     to the expression data for each probe. The expression data should
     be log-ratios for two-color array platforms or log-expression
     values for one-channel platforms. (To fit linear models to the
     individual channels of two-color array data, see 'lmscFit'.) The
     coefficients of the fitted models describe the differences between
     the RNA sources hybridized to the arrays. The genewise fitted
     model results are stored in a compact form suitable for further
     processing by other functions in the limma package.

     If 'object' is a matrix then it should contain log-ratios or
     log-expression data with rows corresponding to probes and columns
     to arrays. (A numeric vector is treated the same as a matrix with
     one column.) For objects of other classes, a matrix of expression
     values is taken from the appropriate component or slot of the
     object. If 'object' is of class 'MAList' or 'marrayNorm', then the
     matrix of log-ratios (M-values) is extracted. If 'object' is of
     class 'exprSet' then the 'exprs' slot is extracted. (This may
     contain log-expression or log-ratio values, depending on the
     platform.) If 'object' is of class 'PLMset' then the matrix of
     chip coefficients 'chip.coefs' is extracted.

     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. On the other hand, if any of
     these are set in the function call then they will over-ride the
     slots or components in the data 'object'. If 'object' is an
     'PLMset', then weights are computed as
     '1/pmax(object@se.chip.coefs, 1e-05)^2'. If 'object' is an
     'exprSet' object, then weights are not computed.

     The actual linear model computations are done by passing the data
     to one the lower-level functions 'lm.series', 'gls.series' or
     'rlm.series'. The function 'rlm.series' is used if
     'method="robust"'. If 'method="ls"', then 'gls.series' is used if
     a correlation structure has been specified, i.e., if 'ndups>1' or
     'block' is non-null and 'correlation' is different from zero. If
     'method="ls"' and there is no correlation structure, 'lm.series'
     is used.

_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.

