normalize.loess             package:affy             R Documentation

_S_c_a_l_e _m_i_c_r_o_a_r_r_a_y _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Normalizes arrays using loess.

_U_s_a_g_e:

     normalize.loess(mat, subset = sample(1:(dim(mat)[1]), min(c(5000,
                      nrow(mat)))), epsilon = 10^-2, maxit = 1, log.it =
                      TRUE, verbose = TRUE, span = 2/3, family.loess =
                      "symmetric")
     normalize.AffyBatch.loess(abatch,type=c("together","pmonly","mmonly","separate"), ...)

_A_r_g_u_m_e_n_t_s:

     mat: a matrix with columns containing the values of the chips to
          normalize.

  abatch: an 'AffyBatch' object.

  subset: a subset of the data to fit a loess to.

 epsilon: a tolerance value (supposed to be a small value - used as a
          stopping criterium).

   maxit: maximum number of iterations.

  log.it: logical. If 'TRUE' it takes the log2 of 'mat'

 verbose: logical. If 'TRUE' displays current pair of chip being worked
          on.

    span: parameter to be passed the function 'loess'

family.loess: parameter to be passed the function 'loess'. '"gaussian"'
          or '"symmetric"' are acceptable values for this parameter.

    type: A string specifying how the normalization should be applied.
          See details for more.

     ...: any of the options of normalize.loess you would like to
          modify (described above).

_D_e_t_a_i_l_s:

     The type arguement should be one of
     '"separate","pmonly","mmonly","together"' which indicates whether
     to normalize only one probe type (PM,MM) or both together or
     separately.

_S_e_e _A_l_s_o:

     'normalize'

_E_x_a_m_p_l_e_s:

          #require(affy)
          #data(Dilution)
          #x <- pm(Dilution[,1:3])
          #mva.pairs(x)
          #x <- normalize.loess(x,subset=1:nrow(x))
          #mva.pairs(x)

