lm.series               package:limma               R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l _t_o _M_i_c_r_o_r_r_a_y _D_a_t_a _b_y _O_r_d_i_n_a_r_y _L_e_a_s_t _S_q_u_a_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a linear model genewise to expression data from a series of
     arrays. This function uses ordinary least squares and is a utility
     function for 'lmFit'.

_U_s_a_g_e:

     lm.series(M,design=NULL,ndups=1,spacing=1,weights=NULL)

_A_r_g_u_m_e_n_t_s:

       M: numeric matrix containing log-ratio or log-expression values
          for a series of microarrays, rows correspond to genes and
          columns to arrays

  design: numeric design matrix defining the linear model. The number
          of rows should agree with the number of columns of M. The
          number of columns will determine the number of coefficients
          estimated for each gene.

   ndups: number of duplicate spots. Each gene is printed ndups times
          in adjacent spots on each array.

 spacing: the spacing between the rows of 'M' corresponding to
          duplicate spots, 'spacing=1' for consecutive spots

 weights: an optional numeric matrix of the same dimension as 'M'
          containing weights for each spot. If it is of different
          dimension to 'M', it will be filled out to the same size.

_D_e_t_a_i_l_s:

     This is a utility function used by the higher level function
     'lmFit'. Most users should not use this function directly but
     should use 'lmFit' instead.

     The linear model is fit for each gene by calling the function
     'lm.fit' or 'lm.wfit' from the base library.

_V_a_l_u_e:

     A list with components 

coefficients: numeric matrix containing the estimated coefficients for
          each linear model. Same number of rows as 'M', same number of
          columns as 'design'.

stdev.unscaled: numeric matrix conformal with 'coef' containing the
          unscaled standard deviations for the coefficient estimators.
          The standard errors are given by 'stdev.unscaled * sigma'.

   sigma: numeric vector containing the residual standard deviation for
          each gene.

df.residual: numeric vector giving the degrees of freedom corresponding
          to 'sigma'.

      qr: QR-decomposition of 'design'

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'lm.fit'.

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

