arrayWeights              package:limma              R Documentation

_A_r_r_a_y _Q_u_a_l_i_t_y _W_e_i_g_h_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Estimates relative quality weights for each array in a multi-array
     experiment with replication.

_U_s_a_g_e:

     arrayWeights(object, design = NULL, weights = NULL, method = "genebygene", maxiter = 50, tol = 1e-15, trace=FALSE)

_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.

 weights: optional numeric matrix containing prior weights for each
          spot.

  method: character string specifying the estimating algorithm to be
          used. Choices are '"genebygene"' and '"reml"'.

 maxiter: maximum number of iterations allowed.

     tol: convergence tolerance.

   trace: logical variable. If true then output diagnostic information
          at each iteration of '"reml"' algorithm.

_D_e_t_a_i_l_s:

     The relative reliability of each array is estimated by measuring
     how well the expression values for that array follow the linear
     model.

     A heteroscedastic model is fitted to the expression values for 
     each gene by calling the function 'lm.wfit'.  The dispersion model
      is fitted to the squared residuals from the mean fit, and is set
     up to  have array specific coefficients, which are updated in
     either full REML  scoring iterations, or using an efficient
     gene-by-gene update algorithm.   The final estimates of these
     array variances are converted to weights.

     The arguments 'design' and 'weights' will be extracted from the
     data  'object' if available and do not normally need to be set
     explicitly in  the call; if any of these are set in the call then
     they will over-ride  the slots or components in the data 'object'.

     If 'object' is a 'PLMset', then expression values will be taken
     from the slot 'chip.coefs' and weights will be computed from
     'se.chip.coefs'. If 'object' is an 'exprSet', then expression
     values will be taken from the 'exprs' slot, but weights will not
     be computed.

_V_a_l_u_e:

     A matrix of array weights, suitable for use in the 'weights'
     argument of 'lmFit'.

_A_u_t_h_o_r(_s):

     Matthew Ritchie

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     ## Not run: 
     array.wts <- arrayWeights(MA, design)
     fit.wts <- lmFit(MA, design, weights=array.wts)
     ## End(Not run)

