| 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]