mni.compute.FDR: Compute the q value at every vertex
In BIC-MNI/mni.cortical.statistics: Regression analyses on the surface

Description Usage Arguments Details Value See Also

Uses the False Discovery Rate as described by Genovese et. al. to compute both the q-values and a t-threshold for a corresponding set of t or p values.

1 2	mni.compute.FDR(t.stats=NULL, p.values=NULL, filename=NULL, column.name=NULL, df=Inf, fdr=0.05, plot.fdr=FALSE)

`t.stats`	An array of t-statistics.
`p.values`	An array of p-values.
`filename`	Quoted filename of previous results
`column.name`	The column of the file specified by filename to retrieve
`df`	The degrees of freedom.
`fdr`	The threshold to test for.
`plot.fdr`	Whether to produce a plot of the FDR procedure.

This function computes the False Discovery Rate as described by the NeuroImage paper by Genovese et. al. It can handle three possible inputs: an array of p values, and array of t statistics, or a filename of vertstats results (as generated by mni.write.vertex.stats). In the latter case, the filename should be quoted and accompanied by a column name (specified by the column.name argument, also quoted). If you are unsure about the possible column names in that file, leave that argument out and mni.compute.FDR will report an error but also give a list of the possible column names in that file.

Returns both the threshold as well as a list of q-values corresponding to the t or p values.

mni.read.glim.file. mni.build.data.table. mni.vertex.statistics. mni.mean.statistics. mni.write.vertex.stats.