fitWithin              package:stepNorm              R Documentation

_S_i_m_p_l_e _l_o_c_a_t_i_o_n _n_o_r_m_a_l_i_z_a_t_i_o_n _f_u_n_c_t_i_o_n _f_o_r _c_D_N_A _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:

     This function performs location normalization on cDNA micoroarray.
     It operates on class 'marrayRaw' or class 'marrayNorm'. It allows 
     the user to choose from a set of three basic normalization
     procedures.

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

     fitWithin(x.fun = "maA", y.fun = "maM", z.fun = TRUE, subset=TRUE, fun = "medfit", ...)

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

   x.fun: Name of accessor method for spot intensity, usually 'maA'.

   y.fun: Name of accessor method for spot statistics, usually the
          log-ratio 'maM'.

   z.fun: Name of accessor method for spot statistic used to stratify
          the data, usually a layout parameter, e.g. 'maPrintTip' or
          'maCompPlate'. If 'z' is not a character, e.g. NULL,  the
          data are not stratified.

  subset: A "logical" or "numeric" vector indicating the subset of
          points used to compute the  normalization values.

     fun: Character string specifying the normalization procedure:

          _m_e_d_f_i_t for global median location normalization

          _r_l_m_f_i_t for global intensity or A-dependent location
               normalization using  the 'rlm' function

          _l_o_e_s_s_f_i_t for global intensity or A-dependent location
               normalization using  the 'loess' function

     ...: Miscs arguments to be passed in 'fun'

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

     Normalization is typically performed on the expression ratios of
     cDNA microarray data, using the function specified by argument
     'fun'. Currently, this function is to be chosen from: 'medfit'
     (median), 'rlmfit' (rlm) and 'loessfit'(loess). When 'z.fun' is
     provided as a character string, for example, 'maPrintTip', the
     normalization procedure is operated within each print-tip of the
     slide.

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

     The function 'fitWithin' returns a function(F) with bindings for
     'x.fun', 'y.fun', 'z.fun', 'subset' and 'fun'. When the function F
     is evaluated with an object of class 'marrayNorm' or 'marrayRaw',
     it carries out normalization and returns an object of class
     'marrayFit' that contains the normalization information as a list
     with the following  list components: 

  varfun: : A character vector of names of predictor variables.

       x: : A numeric matrix of predictor variables.

       y: : A numeric matrix of repsonses.

residuals: : A numeric matrix of normalized values (typically log
          ratios (M)).

  fitted: : A numeric matrix of the fitted values.

     enp: : The equivalent number of parameters; see 'loess'.

df.residual: : The residual degrees of freedom.

     fun: : A character string indicating the name of the function used
          for normalization.

     Note that the 'residuals' component stores the normalized ratios.

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

     Yuanyuan Xiao, yxiao@itsa.ucsf.edu, 
      Jean Yee Hwa Yang, jean@biostat.ucsf.edu

_R_e_f_e_r_e_n_c_e_s:

     Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001).
     Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen,
     A. N. Dorsel, and E. R. Dougherty (eds), _Microarrays: Optical
     Technologies and Informatics_, Vol. 4266 of _Proceedings of SPIE_.

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

     'fit2DWithin'

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

     ## using the swirl data as example
     data(swirl)

     ## median normalization
     med <- fitWithin(fun="medfit")
     swirl1.med <- med(swirl[,1])
     norm.M <- swirl1.med$residuals ## matrix of normalized ratios

     ## rlm normalization
     rlmF <- fitWithin(fun="rlmfit")
     swirl1.rlm <- rlmF(swirl[,1])

     ## loess normalization, default span=0.4
     loessF <- fitWithin(fun="loessfit")
     swirl1.loess <- loessF(swirl[,1])
     ## loess normalization, span=0.2
     loessF.1 <- fitWithin(fun="loessfit", span=0.2)
     swirl1.loess.1 <- loessF.1(swirl[,1])

     ## within-printtip loess normalization
     loessP <- fitWithin(z.fun="maPrintTip", fun="loessfit")
     swirl1.loessP <- loessP(swirl[,1])

