On 10/16/2013 06:32 AM, John linux-user wrote:
Hello everyone,
I am wondering how to simply merge two GRanges objects by range field and add
the value by additional vector. For example, I have two objects below
Hi -- GRanges is from a Bioconductor package, so please ask on the Bioconductor
mailing list
http://bioconductor.org/help/mailing-list/
I think you might do hits = findOverlaps(obj1, obj2) to get indexes of
overlapping ranges, then pmin(obj1[queryHits(obj1)], obj2[subjectHits(obj2)])
and pmax() to get start and end coordinates, and construct a new GRanges from
those. If you provide an easily reproducile example (e.g., constructing some
sample GRanges objects 'by hand' using GRanges()) and post to the Bioconductor
mailing list you'll likely get a complete answer.
Martin
obj1
seqnames ranges strand | Val
<Rle> <IRanges> <Rle> | <integer>
[1] chr1_random [272531, 272571] + | 88
[2] chr1_random [272871, 272911] + | 45
obj2
seqnames ranges strand | Val
<Rle> <IRanges> <Rle> | <integer>
[1] chr1_random [272531, 272581] + | 800
[2] chr1_random [272850, 272911] + | 450
after merged, it should be an object as the following mergedObject and it would
concern the differences in IRANGE data (e.g. 581 and 850 in obj2 above were
different from those of obj1, which were 571 and 871 respectively)
mergedObject
seqnames ranges strand | object2Val
object1Val
<Rle> <IRanges> <Rle> | <integer>
<integer>
[1] chr1_random [272531, 272581] + | 800 88
[2] chr1_random [272850, 272911] + | 450 45
On Wednesday, October 16, 2013 8:31 AM, Terry Therneau <thern...@mayo.edu>
wrote:
On 10/16/2013 05:00 AM, r-help-requ...@r-project.org wrote:
Hello,
I'm trying to use coxph() function to fit a very simple Cox proportional
hazards regression model (only one covariate) but the parameter space is
restricted to an open set (0, 1). Can I still obtain a valid estimate by
using coxph function in this scenario? If yes, how? Any suggestion would be
greatly appreciated. Thanks!!!
Easily:
1. Fit the unrestricted model. If the solution is in 0-1 you are done.
2. If it is outside, fix the coefficient. Say that the solution is 1.73,
then the
optimal solution under contraint is 1.
Redo the fit adding the paramters "init=1, iter=0". This forces the
program to
give the loglik and etc for the fixed coefficient of 1.0.
Terry Therneau
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