| rmaFitUnit {flagme} | R Documentation |
Using rlm from MASS, this procedure fits a linear model using all the fragments
rmaFitUnit(u,maxit=5,mzEffect=TRUE,cls=NULL,fitSample=TRUE,fitOrCoef=c("coef","fit"),TRANSFORM=log2)
u |
a metabolite unit (list object with vectors mz and rt for m/z and retention times, respectively and a data element giving the fragmentxsample intensitity matrix) |
maxit |
maximum number of iterations (default: 5) |
mzEffect |
logical, whether to fit m/z effect (default: TRUE) |
cls |
class variable |
fitSample |
whether to fit individual samples (alternative is fit by group) |
fitOrCoef |
whether to return a vector of coefficients (default: "coef"), or an rlm object ("fit") |
TRANSFORM |
function to transform the raw data to before fitting (default: log2) |
Fits a robust linear model.
list giving elements of fragment and sample coefficients (if fitOrCoef="coef") or a list
of elements from the fitting process (if fitOrCoef="fit")
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
peaksAlignment, clusterAlignment
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])
# pairwise alignment using all scans
fullca<-clusterAlignment(pd, usePeaks = FALSE, df = 100)
# calculate retention time shifts
timedf<-calcTimeDiffs(pd, fullca)