p.int2 {OLIN} | R Documentation

## Calculates significance of intensity-dependent bias

### Description

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.

### Usage

    p.int2(object,delta=50,N=-1,av="median",p.adjust.method="none")

### Arguments

`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`

### Details

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`.

### Note

This function will be merged with `p.int` in future versions.

### Author(s)

Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)

### See Also

`p.int`,`fdr.int2`, `sigint.plot2`, `p.adjust`

### Examples

    # 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

[Package OLIN version 1.16.0 Index]