tranestmult {LMGene} | R Documentation |
A sub-function of tranest
which searches the best parameters for glog transformation.
tranestmult (eS, starting = FALSE, lambda = 1000, alpha = 0, gradtol = 0.001, lowessnorm=FALSE, method=1, max_iter=200, model=NULL)
eS |
Array data. must be an ExpressionSet object. |
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 |
Set optimization method; default is modified Gauss-Newton (nlm). See tranest . |
max_iter |
Max. number of iterations of nlm to use in optimization. |
model |
Model in terms of vlist which is compared to transformed expression data. See tranest . |
This is primarily an internal function. The normal way of calling it would
be to call tranest
with the option mult=TRUE.
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.
The model
argument is an optional character string, constructed like the right-hand
side of a formula for lm. It specifies which of the variables in the ExpressionSet
will
be used in the model and whether interaction terms will be included. If model=NULL
,
it uses all variables from the ExpressionSet
without interactions. Be careful of using
interaction terms with factors; this often leads to overfitting, which will yield an error.
tranpar |
A list (not a vector) containing the best parameter for 'lambda' and the best vector for 'alpha'. |
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) tranpar <- tranestmult(sample.eS, lambda= 500, alpha=50) tranpar