Description Usage Arguments Value Author(s) See Also Examples
View source: R/FDR.permutation.R
FDR.permutation
will first run GUESS in
parallel (over CPUs) for several permutations of the Y
matrix
mimicking the null hypothesis of no association. Results from the
permutation procedure will subsequently be used to derive the
cut-off values for the Marginal Posterior Probability of Inclusion
(MPPI
) ensuring an empirical FDR control at a user-defined
level. Several cut-off values will be investigated and, for each, the
corresponding empirical FDR will be returned. The cut-off value
providing the closest FDR estimate to the desired level will be
retained. Exact match to the desired level will be achieved by linear
interpolation. The latter is based on the same calculation as in
Analysis.permutation
.
1 2 | FDR.permutation(x,path.input = NULL, Npermut, start.counter=1,
path.output = NULL, threshold = 0.05, nbcpu = NULL, number.cutoff=200)
|
x |
an object of class |
path.input |
path to the directory containing the permuted re-samples of
the |
path.output |
path to the directory in which results from
permuted data are stored. By default (=NULL), these are saved in
the directory where results from the original |
Npermut |
number of permutations to run. |
start.counter |
defines the integer from which to start labelling permutation runs. |
threshold |
numeric value specifying the desired FDR level. |
nbcpu |
integer indicating the number of CPUs to use for the permutation procedure. This number has to be lower than the number of cores available on the platform. By default (=NULL), the function uses a single core. |
number.cutoff |
numeric value specifying the number of points on which to base the FDR estimation. |
FDR.permutation
generates permutation re-samples from
the original Y
matrix and generates for each permutation
standard R2GUESS
output files. Sets of results can be separately
analysed using Analysis.permutation
. The function also
returns a list containing the following fields:
cutoff.MPI |
the MPPI threshold to control empirical FDR at a specified level. |
cutoff_int |
the linearly interpolated (across the
|
cutoff_St |
the vector of cut-off values investigated (containing
|
FDR_emp |
empirical FDR corresponding to the |
FDR_emp_int |
empirical FDR value estimated by linear interpolation for the |
FDR_emp_St |
a vector of empirical FDR values computed by
linear interpolation for each |
Benoit Liquet b.liquet@uq.edu.au,
Marc Chadeau-Hyam m.chadeau@imperial.ac.uk,
Leonardo
Bottolo l.bottolo@imperial.ac.uk,
Gianluca Campanella
g.campanella11@imperial.ac.uk
1 2 3 4 5 6 7 8 9 | ## Not run:
modelY_Hopx <- example.as.ESS.object()
path.output.perm <- tempdir()
path.input.perm <-path.output.perm
cutoff <- FDR.permutation(x=modelY_Hopx,Npermut=100,start.counter=1,
path.output=path.output.perm,path.input=path.input.perm,nbcpu=3)
## End(Not run)
|
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