modelMatrix              package:limma              R Documentation

_C_o_n_s_t_r_u_c_t _D_e_s_i_g_n _M_a_t_r_i_x

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

     Construct design matrix from RNA target information.

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

     modelMatrix(targets, parameters, ref, verbose=TRUE)
     uniqueTargets(targets)

_A_r_g_u_m_e_n_t_s:

 targets: matrix or data.frame with columns 'Cy3' and 'Cy5' specifying
          which RNA was hybridized to each array

parameters: matrix specifying contrasts between RNA samples which
          should correspond to regression coefficients. Row names
          should correspond to unique RNA sample names found in
          'targets'.

     ref: character string giving name of common reference RNA if such
          exists. Exactly one argument of 'parameters' or 'ref' should
          be specified.

 verbose: logical, if 'TRUE' then unique names found in 'targets' will
          be printed to standard output

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

     This function is intended to produce a design matrix for use in
     functions 'lmFit' etc for two-color microarray experiments.

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

     'modelMatrix' produces a design matrix with row names as in
     'targets' and column names as in 'parameters'.

     'uniqueTargets' produces a character vector of unique target names
     from the columns 'Cy3' and 'Cy5' of 'targets'.

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

     Gordon Smyth

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

     'model.matrix' in the stats package.

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

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

     targets <- cbind(Cy3=c("Ref","Control","Ref","Treatment"),Cy5=c("Control","Ref","Treatment","Ref"))
     rownames(targets) <- paste("Array",1:4)

     parameters <- cbind(C=c(-1,1,0),T=c(-1,0,1))
     rownames(parameters) <- c("Ref","Control","Treatment")

     modelMatrix(targets, parameters)
     modelMatrix(targets, ref="Ref")

