tranest {LMGene} | R Documentation |
Finds the best parameters for glog transformation.
tranest(eS, ngenes = -1, starting = FALSE, lambda = 1000, alpha = 0, gradtol = 0.001, lowessnorm = FALSE, method=1, mult=FALSE, model=NULL)
eS |
Array data. must be exprSet type. |
ngenes |
Number of genes that is going to be used for the parameter estimation |
starting |
TRUE, if the given initial parameter values are used |
lambda |
Initial parameter value for lambda |
alpha |
Initial parameter value for alpha |
gradtol |
a positive scalar giving the tolerance at which the scaled gradient is considered close enough to zero to terminate the algorithm |
lowessnorm |
TRUE, if lowess method is going to be used |
method |
Determines optimization method. Default is 1, which corresponds to a Newton-type method (see nlm ). Method 2 is based on the Nelder-Mead method (see optim ). |
mult |
If true, tranest will use a vector alpha with one entry per sample. Default is false (same alpha for every sample). |
model |
Specifies model to be used. Default is to use all variables from eS without interactions. See details. |
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.
'model' is an optional character string, constructed like the right-hand side of a formula for lm. It specifies which of the variables in the exprSet will be used in the model and whether interaction terms will be included. If model=NULL, it uses all variables from the exprSet without interactions. Be careful of using interaction terms with factors: this often leads to overfitting, which will yield an error.
tranpar |
A list containing the best parameter for 'lambda' and 'alpha' |
David Rocke, Geun-Cheol Lee and John Tillinghast
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) tranpar <- tranest(sample.eS, 100) tranpar tranpar <- tranest(sample.eS, mult=TRUE) tranpar