"estimateTagwiseDisp"(y, prior.df=10, trend="movingave", span=NULL, method="grid", grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...) "estimateTagwiseDisp"(y, group=NULL, lib.size=NULL, dispersion, AveLogCPM=NULL, prior.df=10, trend="movingave", span=NULL, method="grid", grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...)
"grid"(default) for interpolation on grid points or
"optimize"to call the function of the same name.
method="grid", the number of points on which the interpolation is applied for each tag.
method="grid", the range of the grid points around the trend on a log2 scale.
method="optimize", the tolerance for Newton-Rhapson iterations.
TRUEthen diagnostic ouput is produced during the estimation process.
estimateTagwiseDisp.DGEListadds the following components to the input
estimateTagwiseDisp.defaultreturns a numeric vector of the tagwise dispersion estimates.
The prior values for the dispersions are determined by a global trend. The individual tagwise dispersions are then squeezed towards this trend. The prior degrees of freedom determines the weight given to the prior. The larger the prior degrees of freedom, the more the tagwise dispersions are squeezed towards the global trend. If the number of libraries is large, the prior becomes less important and the tagwise dispersion are determined more by the individual tagwise data.
trend="none", then the prior dispersion is just a constant, the common dispersion.
Otherwise, the trend is determined by a moving average (
trend="movingave") or loess smoother applied to the tagwise conditional log-likelihood.
method="loess" applies a loess curve of degree 0 as implemented in
method="optimize" is not recommended for routine use as it is very slow.
It is included for testing purposes.
Note that the terms `tag' and `gene' are synonymous here. The function is only named `Tagwise' for historical reasons.
estimateCommonDispis usually run before