run_skewness: Calculate the Skewness of Sample Runs
In pmartR/pmartRqc: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data
View source: R/filter_objects.R
run_skewness R Documentation
Calculate the Skewness of Sample Runs
Description
This function calculates the skewness across data for each sample run.
Usage
run_skewness(data_only)
Arguments
data_only
a p \times n data.frame, where p is the number
of peptides and n is the number of samples.
Details
Skewness is calculated as a bias-corrected calculation given by
method 2 in the e1071 package. Within a sample NA values are
ignorned in the skewness calculation. If all peptide abundance values are
missing within a sample, the skewness is replaced by the overall mean of
nonmissing skewness values for the data.
Value
data.frame with two elements: Sample, a character vector giving the
sample names; and Skewness, a numeric vector giving the skewness values
Author(s)
Lisa Bramer
pmartR/pmartRqc documentation built on April 25, 2024, 6:18 a.m.
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View source: R/filter_objects.R
| run_skewness | R Documentation |
Calculate the Skewness of Sample Runs
Description
This function calculates the skewness across data for each sample run.
Usage
run_skewness(data_only)
Arguments
data_only |
a |
Details
Skewness is calculated as a bias-corrected calculation given by
method 2 in the e1071 package. Within a sample NA values are
ignorned in the skewness calculation. If all peptide abundance values are
missing within a sample, the skewness is replaced by the overall mean of
nonmissing skewness values for the data.
Value
data.frame with two elements: Sample, a character vector giving the sample names; and Skewness, a numeric vector giving the skewness values
Author(s)
Lisa Bramer
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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