[BioC] how to rank affy probesets by their probe-effect magnitude

[Robert Castelo]

dear list,

i'm searching for a way to rank affy probesets from classical 3' affy
arrays by their probe effect magnitude. i mean that i would like to know
if a probeset is has a larger probe-specific effect than another one.

i guess the solution should be in the affyPLM package since if i do

library(affy)
library(affyPLM)

ab <- ReadAffy()
pset <- fitPLM(ab)


i obtain an object (pset) of the PLMset class which contains slots
'probe.coefs' and 'se.probe.coefs', where each is a list as many keys as
probesets and where each probeset contains information on the probe
effect of each probe within the probeset:

head(names(pset at probe.coefs))
[1] "1000_at"   "1001_at"   "1002_f_at" "1003_s_at" "1004_at"
"1005_at"  
head(names(pset at se.probe.coefs))
[1] "1000_at"   "1001_at"   "1002_f_at" "1003_s_at" "1004_at"
"1005_at"  

pset at probe.coefs[[1]]
             Overall
probe_1   0.97287528
probe_2   0.61454806
probe_3  -2.81701693
probe_4   1.68063395
probe_5  -3.31991235
probe_6   1.56657388
probe_7  -3.30256264
probe_8  -1.99431231
probe_9  -0.35200585
probe_10 -0.49024387
probe_11 -1.09087811
probe_12  0.22008832
probe_13  2.54263342
probe_14  3.71106614
probe_15  2.12580554
probe_16 -0.06729251

pset at se.probe.coefs[[1]]
            Overall
probe_1  0.06124122
probe_2  0.06039453
probe_3  0.06180433
probe_4  0.05948503
probe_5  0.06727454
probe_6  0.06016827
probe_7  0.06233682
probe_8  0.06791376
probe_9  0.05960599
probe_10 0.05963511
probe_11 0.05868359
probe_12 0.06046023
probe_13 0.05885199
probe_14 0.05829506
probe_15 0.05837877
probe_16 0.06340662

however, i'm unsure how to proceed from now on to decide whether a
particular probeset is more "affected" by probe-specific effects than
other probeset. any suggestion would be highly appreciated,

thanks,
robert.