Function reference • CompMetaboTools

All functions
`c(<MChromatograms>)` `c(<XChromatograms>)`	Combine MChromatograms objects
`chromPeakArea()`	Get MS peak area (m/z and rt range) for chromatographic peaks
`dropModels()`	Remove model fits based on specified criteria The `dropModels` function takes model fits such as the ones returned by `xcms:::rowFitModel` or `withinBatchFit()` and removes models idenfified by function `FLAG_FUN`. This function can be one of the `flag_*` functions (such as `flag_model_residual()`) that take a linear model fit as input and return a logical indicating whether the model fit is problematic or not.
`extract_time_stamp()`	Extract run start time stamp
`featureGroups(<XCMSnExp>)` `featureGroups<-`(<XCMSnExp>)	Grouping of LC-MS features
`featureGroupSpectra()` `featureGroupPseudoSpectrum()` `featureGroupFullScan()`	Extract spectra for feature groups
`groupByCorrelation()`	Group rows in a matrix based on their correlation
`groupEicCorrelation()`	Group EICs based on their correlation
`groupFeatures(<XCMSnExp>,<AbundanceSimilarityParam>)`	Group features based on similarity of abundances across samples
`EicCorrelationParam()` `groupFeatures(<XCMSnExp>,<EicCorrelationParam>)`	Group features based on correlation of extracted ion chromatograms
`groupFeatures(<XCMSnExp>,<SimilarRtimeParam>)`	Group features based on similar retention times
`groupToSinglePolarityPairs()`	Sub-group allowing only single positive/polarity pairs per group
`joyPlot(<MChromatograms>)` `joyPlot(<XCMSnExp>)`	Create a stacked plot of multiple chromatograms
`matchRtMz()`	Match features based on their retention time and m/z values
`flag_model_residual()` `flag_model_mean_residual()` `flag_model_inj_range()` `flag_model_cat_count()` `flag_model_coef_count()`	Functions to flag/exclude models
`moreAreValidThan()`	Identify rows with a minimum required proportion of non-missing values
`plotFeatureGroups()`	Plot feature groups in the m/z-retention time space
`plotOverlay()`	Overlay plots from multiple EICs
`plot_pca()`	Plot of PCA results `plot_pca` is a simple utility function to plot the results from a PCA analysis.
`rsd()` `rowRsd()`	Calculate relative standard deviations `rsd` and `rowRsd` are convenience functions to calculate the relative standard deviation (i.e. coefficient of variation) of a numerical vector or for rows of a numerical matrix, respectively.
`sync_files_local()` `remove_local_files()`	Create local copies of files
`withinBatchAdjust()`	Apply per-batch model adjustment of feature abundances `withinBatchAdjust` adjusts feature abundances within each batch based on the model provided with parameter `models` (which are fitted using the `withinBatchFit()` function.
`withinBatchFit()`	Fit a model separately to each batch in an experiment
`xdata`	LC-MS preprocessing result test data

Reference

All functions