find.tol: An internal function that is not supposed to be directly...
In yufree/apLCMS: Adaptive processing of LC-MS data
find.tol R Documentation
An internal function that is not supposed to be directly accessed by the user. Find m/z tolerance level.
Description
The function finds the tolerance level in m/z from a given vector of observed m/z values.
Usage
find.tol(a, uppermost=1e-4, aver.bin.size=4000, min.bins=50, max.bins=200)
Arguments
a
The vector of observed m/z values.
uppermost
Consider only m/z diffs smaller than this value.
aver.bin.size
The average bin size to determine the number of equally spaced points in the kernel density estimation.
min.bins
the minimum number of bins to use in the kernel density estimation. It overrides aver.bin.size when too few observations are present.
max.bins
the maximum number of bins to use in the kernel density estimation. It overrides aver.bin.size when too many observations are present.
Details
The method assumes a mixture model: an unknown distribution of m/z variations in the same peak, and an exponential distribution of between-peak diffs. The parameter of the exponential distribution is estimated by the upper 75
Value
The tolerance level is returned.
Author(s)
Tianwei Yu <tyu8@emory.edu>
Examples
data(prof)
find.tol(prof[[1]][,1])
yufree/apLCMS documentation built on May 19, 2024, 1:22 p.m.
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| find.tol | R Documentation |
An internal function that is not supposed to be directly accessed by the user. Find m/z tolerance level.
Description
The function finds the tolerance level in m/z from a given vector of observed m/z values.
Usage
find.tol(a, uppermost=1e-4, aver.bin.size=4000, min.bins=50, max.bins=200)
Arguments
a |
The vector of observed m/z values. |
uppermost |
Consider only m/z diffs smaller than this value. |
aver.bin.size |
The average bin size to determine the number of equally spaced points in the kernel density estimation. |
min.bins |
the minimum number of bins to use in the kernel density estimation. It overrides aver.bin.size when too few observations are present. |
max.bins |
the maximum number of bins to use in the kernel density estimation. It overrides aver.bin.size when too many observations are present. |
Details
The method assumes a mixture model: an unknown distribution of m/z variations in the same peak, and an exponential distribution of between-peak diffs. The parameter of the exponential distribution is estimated by the upper 75
Value
The tolerance level is returned.
Author(s)
Tianwei Yu <tyu8@emory.edu>
Examples
data(prof)
find.tol(prof[[1]][,1])
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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