EIC.plot.learn: Plot extracted ion chromatograms based on the machine...
In yufree/apLCMS: Adaptive processing of LC-MS data

Description Usage Arguments Details Value Author(s) References

Given an output object from the function semi.sup.learn(), this function plots the EICs selected by the user.

EIC.plot.learn(aligned, rows = NA, colors = NA, transform = "none",
                 subset = NA, tol = 2.5e-05, ridge.smoother.window =
                 50, baseline.correct = 0, max.spline.time.points =
                 1000)

`aligned`	An output object from cdf.to.ftr().
`rows`	A numeric vector selecting which rows of the aligned feature table to be plotted.
`colors`	The colors (one per profile) the user wishes to use for the plots. The default is NA, in which case a default color set is used.
`transform`	There are four possible values. "none": the original intensity data is plotted; "log": the intensity data is transformed by log(x+1); "sqrt": the intensity data is square root transformed; "cuberoot": the intensity data is cube root transformed.
`subset`	The user can choose a subset of the profiles for which the EICs are plotted. It is given as a vector of profile indecies. The default is NA, in which case the EICs from all the profiles are plotted.
`tol`	The mz tolerance level used in learn.cdf().
`ridge.smoother.window`	The ridge.smoother.window parameter value used in learn.cdf().
`baseline.correct`	The baseline.correct parameter value used in learn.cdf().
`max.spline.time.points`	The maximum number of points to use in the spline fit along the retention time axis.