Description Usage Arguments Details Value Author(s) References See Also Examples
Fits the model described in Li and Wong (2001) to a probe set with I chips and J probes.
1 2 3 4 5 6 7 8 9  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=1e06, 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=1e06, maxit=50,
outer.maxit=50,verbose=FALSE)

data.matrix 
an I x J matrix containing the probe set data. Typically the i,j entry will contain the PMMM value for probe pair j in chip i. Another possible use, is to use PM instead of PMMM. 
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.resid.quantile 
any residual bigger than the

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 
... 
additional arguments. 
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 nonconvergence 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.
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 ( 
phi.outliers 
logical vector describing which probe sets (phis) are
considered outliers ( 
convergence1 
logical value. If 
convergence2 
logical value. If 
iter 
number of iterations needed to achieve convergence. 
delta 
difference between thetas when iteration stopped. 
Rafael A. Irizarry, Cheng Li, Fred A. Wright, Ben Bolstad
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.
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