normalize.loess           package:codelink           R Documentation

_N_o_r_m_a_l_i_z_e_d _c_h_i_p_s _u_s_i_n_g _c_y_c_l_i_c _l_o_e_s_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Takes a matrix and apply cyclic loess normalization. It is based
     in normalize.loess from package affy but supports NA.

_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 = FALSE, span = 2/3, family.loess = "symmetric", weights = NULL)

_A_r_g_u_m_e_n_t_s:

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

  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.

 weights: a vector of weights for the individual measurements.

_V_a_l_u_e:

     A matrix of normalized values.

_A_u_t_h_o_r(_s):

     Diego Diez

_E_x_a_m_p_l_e_s:

     ## Not run: 
             mat <- matrix(sample(500), 100, 5)
             mat <- normalize.loess(mat)
     ## End(Not run)

