estimateGLMCommonDisp {edgeR} | R Documentation |
Estimates a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.
## S3 method for class 'DGEList' estimateGLMCommonDisp(y, design=NULL, offset=NULL, method="CoxReid", ...) ## Default S3 method: estimateGLMCommonDisp(y, design=NULL, offset=NULL, method="CoxReid", ...)
y |
an object that contains the raw counts for each library (the measure of expression level); it can either be a matrix of counts, or a |
design |
numeric matrix giving the design matrix for the GLM that is to be fit. Must be of full column rank. Defaults to a single column of ones, equivalent to treating the columns as replicate libraries. |
method |
method for estimating the dispersion.
Possible values are |
offset |
numeric scalar, vector or matrix giving the offsets for the log-linear models.
If a scalar, then this value will be used as an offset for all transcripts and libraries.
If a vector, it should be have length equal to the number of libraries, and the same vector of offsets will be used for each transcript.
If a matrix, then it should have the same row and column dimensions as In the |
... |
other arguments are passed to lower-level functions.
See |
This function calls dispCoxReid
, dispPearson
or dispDeviance
depending on the method
specified.
See dispCoxReid
for details of the three methods and a discussion of their relative performance.
The default method returns a numeric vector of length 1 containing the estimated dispersion.
The DGEList
method returns the same DGEList
y
as input but with common.dispersion
as an added component.
Gordon Smyth
Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
dispCoxReid
, dispPearson
, dispDeviance
estimateGLMTrendedDisp
for trended dispersion and estimateGLMTagwiseDisp
for tagwise dispersions in the context of a generalized linear model.
estimateCommonDisp
for common dispersion or estimateTagwiseDisp
for tagwise dispersion in the context of a multiple group experiment (one-way layout).
# True dispersion is 1/size=0.1 y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4) d <- DGEList(counts=y,group=c(1,1,2,2)) design <- model.matrix(~group, data=d$samples) d1 <- estimateGLMCommonDisp(d, design) d1$common.disp # Compare with classic CML estimator: d2 <- estimateCommonDisp(d) d2$common.disp # See example(glmFit) for a different example