p.int2 {OLIN} | R Documentation |
This function assesses the significance of intensity-dependent bias. This is achieved by comparing the observed average values of logged fold-changes within an intensity neighbourhood with an empirical distribution generated by permutation tests. The significance is given by (adjusted) p-values.
p.int2(object,delta=50,N=-1,av="median",p.adjust.method="none")
object |
object of class marrayRaw or marrayNorm |
delta |
integer determining the size of the neighbourhood (2 * delta+1 ). |
N |
number of random samples (of size 2 * delta+1 ) used for the
generation of empirical distribution. If N is negative,
the number of samples 100 times the length of A . |
av |
averaging of M within neighbourhood by mean or median (default) |
p.adjust.method |
method for adjusting p-values due to multiple testing regime. The available
methods are “none”, “bonferroni”, “holm”, “hochberg”,
“hommel” and “fdr”. See also p.adjust |
This function p.int2
is basically the same as p.int
except for
differences in their in- and output format. For the details about the functionality, see p.int
.
This function will be merged with p.int
in future versions.
Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)
p.int
,fdr.int2
, sigint.plot2
, p.adjust
# To run these examples, "un-comment" them! # # LOADING DATA NOT-NORMALISED # data(sw) # CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS # For this illustration, N was chosen rather small. For "real" analysis, it should be larger. # P <- p.int2(sw,delta=50,N=10000,av="median",p.adjust.method="none") # VISUALISATION OF RESULTS # sigint.plot2(sw[,1],Sp=P$Pp[[1]],Sn=P$Pn[[1]],c(-5,-5)) # array 1 # sigint.plot2(sw[,3],Sp=P$Pp[[3]],Sn=P$Pn[[3]],c(-5,-5)) # array 3