fit.li.wong               package:affy               R Documentation

_F_i_t _L_i _a_n_d _W_o_n_g _M_o_d_e_l _t_o _a _P_r_o_b_e _S_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     Fits the model described in Li and Wong (2001) to a probe set with
     I chips and J probes.

_U_s_a_g_e:

     fit.li.wong(data.matrix, remove.outliers=TRUE, normal.array.quantile=0.5,
                 normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
                 outlier.fraction=0.14, delta=1e-06, maxit=50,
                 outer.maxit=50,verbose=FALSE, ...)

     li.wong(data.matrix,remove.outliers=TRUE, normal.array.quantile=0.5,
                 normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
                 outlier.fraction=0.14, delta=1e-06, maxit=50,
                 outer.maxit=50,verbose=FALSE)

_A_r_g_u_m_e_n_t_s:

data.matrix: an I x J matrix containing the probe set data. Typically
          the i,j entry will contain the PM-MM value for probe pair j
          in chip i. Another possible use, is to use PM instead of
          PM-MM.

remove.outliers: logical value indicating if the algorithm will remove
          outliers according to the procedure described in Li and Wong
          (2001).

large.threshold: used to define outliers.

normal.array.quantile: quantile to be used when determining what a
          normal SD is. probes or chips having estimates with SDs
          bigger than the quantile 'normal.array.quantile' of all SDs x
          'large.threshold' 

normal.resid.quantile: any residual  bigger than the
          'normal.resid.quantile' quantile of all residuals x
          'large.threshold' is considered an outlier

large.variation: any probe or chip describing more than this much total
          variation is considered an outlier

outlier.fraction: this is the maximum fraction of single outliers that
          can be in the same probe or chip.

   delta: numerical value used to define the stopping criterion. 

   maxit: maximum number of iterations when fitting the model.

outer.maxit: maximum number of iterations of defined outliers.

 verbose: logical value. If 'TRUE' information is given of the status
          of the algorithm.

     ...: 

_D_e_t_a_i_l_s:

     This is Bioconductor's implementation of the Li and Wong
     algorithm. The Li and Wong PNAS 2001 paper was followed. However,
     you will not get the same results as you would get with dChip.
     dChip is not open source so it is not easy to reproduce. 

     Notice that this iterative algorithm will not always converge. If
     you run the algorithm on thousands of probes expect some
     non-convergence warnings. These are more likely when few arrays
     are used. We recommend using this method only if you have 10 or
     more arrays.

     Please refer to references for more details.

_V_a_l_u_e:

     'li.wong' returns a vector of expression measures (or column
     effects) followed by their respective standard error estimates. It
     was designed to work with 'express' which is no longer part of the
     package.

     'fit.li.wong' returns much more. Namely, a list containing the
     fitted parameters and relevant information.  

   theta: fitted thetas.

     phi: fitted phis.

sigma.eps: estimated standard deviation of the error term.

sigma.theta: estimated standard error of theta.

sigma.phi: estimated standard error of phis.

theta.outliers: logical vector describing which chips (thetas) are
          considered outliers ('TRUE').

phi.outliers: logical vector describing which probe sets (phis) are
          considered outliers ('TRUE')

convergence1: logical value. If 'FALSE' the algorithm did not converge
          when fitting the phis and thetas.

convergence2: logical value. If 'FALSE' the algorithm did not converge
          in deciding what are outliers.

    iter: number of iterations needed to achieve convergence.

   delta: difference between thetas when iteration stopped.

_A_u_t_h_o_r(_s):

     Rafael A. Irizarry, Cheng Li, Fred A. Wright, Ben Bolstad

_R_e_f_e_r_e_n_c_e_s:

     Li, C. and Wong, W.H. (2001) _Genome Biology_ *2*, 1-11.

     Li, C. and Wong, W.H. (2001) _Proc. Natl. Acad. Sci USA_ *98*,
     31-36.

_S_e_e _A_l_s_o:

     'li.wong', 'expresso'

_E_x_a_m_p_l_e_s:

         x <- sweep(matrix(2^rnorm(600),30,20),1,seq(1,2,len=30),FUN="+")
         fit1 <- fit.li.wong(x)
         plot(x[1,])
         lines(fit1$theta)
       

