fbinary-methods {msbase} | R Documentation

## Binary measures for pairwise peak-list comparison

### Description

Binary measures are comptued on entries of a contingency table.
Implements the:

- Relative Mutual information,
- Fowlkes Mallows statistics M_11/sqrt(M_1Y*M_1X),
- Gower coefficient (M_01^XY+M_10^XY)/(M_01^XY+M_10^XY+M_11^XY),
- Huberts Γ

### Arguments

`obx` | see below in Methods section
`oby` | see above in Methods section
`error` | measurement error
`ppm` | if `TRUE` then error in parts per million(ppm), in arbitrary units otherwise.
`weight` | should mass accuracy be weighted
`uniq` | if `TRUE` compute non-crossing matching.
`method` | type of dissimilarity: rmi – relative mutual information fm – fowlkes mallows statistics hg – huberts gamma gower – gower coefficient
`N` | default 0 - total length of alinged peak-lists.
`range` | experimental

### Methods

obx = "numeric", oby = "numeric"
  expects two numeric vectors as parameters, returns scalar
obx = "Massvector", oby = "Massvector"
  returns scalar (`numeric`)
obx = "Massvectorlist", oby = "Massvector"
  returns a vector with all pairwise dissimilaritities.
obx = "Massvectorlist", oby = "NULL"
  returns object of class `dist`
obx = "list", oby = "NULL"
  returns object of class `dist`

### Author(s)

Witold E. Wolski witek96@users.sourceforge.net http://r4proteomics.sourceforge.net

### Examples

    # resolve multiple matches.
    data(pldata)
    pl1 <- pldata[[1]]
    pl2 <- pldata[[2]]
    fbinary(pl1,pl2,error=400,ppm=TRUE,theta=1,weight=FALSE,method="rmi",uniq=TRUE,N=0)
    # dont resolve multiple matches.
    fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=FALSE,method="rmi",uniq=FALSE)
    fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=FALSE,method="hg")
    fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="gower")
    fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
    # seach with one peak-list in a list of peak-lists.
    fbinary(pldata,pl1,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
    # compute distances (dissimilarities) and cluster.
    tmp <- fbinary(pldata,NULL,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
    plot(hclust(tmp,method="average"))

[Package msbase version 1.1.1 Index]