normalize.loess {codelink} | R Documentation |
Takes a matrix and apply cyclic loess normalization. It is based in normalize.loess from package affy but supports NA.
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)
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. |
A matrix of normalized values.
Diego Diez
## Not run: mat <- matrix(sample(500), 100, 5) mat <- normalize.loess(mat) ## End(Not run)