| dp {flagme} | R Documentation |
This function calls C code for a bare-bones dynamic programming algorithm, finding the best cost path through a similarity matrix.
dp(M,gap=.5,big=10000000000,verbose=FALSE)
M |
similarity matrix |
gap |
penalty for gaps |
big |
large value used for matrix margins |
verbose |
logical, whether to print out information |
This is a pretty standard implementation of a bare-bones dynamic programming algorithm, with a single gap parameter and allowing only simple jumps through the matrix (up, right or diagonal).
list with element match with the set of pairwise matches.
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
require(gcspikelite)
# paths and files
gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/")
cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
eluFiles<-dir(gcmsPath,"ELU",full=TRUE)
# read data, peak detection results
pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
pd<-addAMDISPeaks(pd,eluFiles[1:2])
# similarity matrix
r<-normDotProduct(pd@peaksdata[[1]],pd@peaksdata[[2]])
# dynamic-programming-based matching of peaks
v<-dp(r,gap=.5)