msecalcmult {LMGene} | R Documentation |
Computes the mean square error and gradient for the global anova
msecalcmult(eS, lam, alpha, lowessnorm=FALSE, R, grads=TRUE)
eS |
Array data. must be exprSet type. |
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 |
grads |
If true, return gradient as well as error. Not used with some kinds of optimization. |
The input argument, eS, must be exprSet type from Biobase package.
If you have a matrix data and information about the considered factors,
then you can use neweS
to conver the data into exprSet.
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)