rowaov {LMGene} | R Documentation |
Computes the mean squares and degrees of freedom for gene-by-gene ANOVAs.
rowaov(eS, model=NULL)
eS |
AArray data. must be an ExpressionSet object and the log-transformation and
the normalization of exprs(eS) are recommended. |
model |
Model used for comparison. See details and LMGene . |
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.
resmat |
A matrix of MSE and DF of all factors for all genes. |
David Rocke and Geun-Cheol Lee
David M. Rocke (2004), Design and analysis of experiments with high throughput biological assay data, Seminars in Cell & Developmental Biology, 15, 703-713.
http://www.idav.ucdavis.edu/~dmrocke/
#library library(Biobase) library(LMGene) #data data(sample.mat) data(vlist) LoggedSmpd0 <- neweS(lnorm(log(sample.mat)),vlist) resmat <- rowaov(LoggedSmpd0) resmat[,1:3]