msecalc {LMGene} | R Documentation |
Computes the mean square error and gradient for the global ANOVA.
msecalc(eS, lam, alpha, lowessnorm, R)
eS |
Array data. must be an ExpressionSet object. |
lam |
A parameter for glog transformation. |
alpha |
A parameter for glog transformation. |
lowessnorm |
TRUE, if lowess method is going to be used. |
R |
The residual matrix, i.e., identity minus the hat matrix. |
The argument eS
must be an ExpressionSet
object from the Biobase package.
If you have a data in a matrix
and information about the considered factors, then you
can use neweS
to convert the data into an ExpressionSet
object. Please see
neweS
in more detail.
msev |
A vector which contains MSE and gradient of two parameters. |
David Rocke and Geun-Cheol Lee
B. Durbin and D.M. Rocke, (2003) Estimation of Transformation Parameters for Microarray Data, Bioinformatics, 19, 1360-1367.
http://www.idav.ucdavis.edu/~dmrocke/
#library library(Biobase) library(LMGene) #data data(sample.eS) lmod <- GetLMObj(sample.eS) X <- lmod$x U <- svd(X)$u H <- crossprod(t(U), t(U)) n <- dim(H)[1] R <- diag(rep(1,n)) - H msecalc(sample.eS,500,50, FALSE, R)