eigen_pi: Compute PI - proportion of observations missing completely at...
In ProteoMM: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform
Description
Usage
Arguments
Value
Examples
Description
Compute PI - proportion of observations missing completely at random
Usage
1
Arguments
m
matrix of abundances, numsmaples x numpeptides
toplot
TRUE/FALSE plot mean vs protportion missing curve and PI
Value
pi estimate of the proportion of
observations missing completely at random
Contributed by Shelley Herbrich & Tom Taverner for Karpievitch et al. 2009
Examples
1
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5
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7
data(mm_peptides)
intsCols = 8:13
metaCols = 1:7
m_logInts = make_intencities(mm_peptides, intsCols)
m_prot.info = make_meta(mm_peptides, metaCols)
m_logInts = convert_log2(m_logInts)
my.pi = eigen_pi(m_logInts, toplot=TRUE)
ProteoMM documentation built on Nov. 8, 2020, 5:57 p.m.
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Description Usage Arguments Value Examples
Description
Compute PI - proportion of observations missing completely at random
Usage
1 |
Arguments
m |
matrix of abundances, numsmaples x numpeptides |
toplot |
TRUE/FALSE plot mean vs protportion missing curve and PI |
Value
pi estimate of the proportion of observations missing completely at random
Contributed by Shelley Herbrich & Tom Taverner for Karpievitch et al. 2009
Examples
1 2 3 4 5 6 7 | data(mm_peptides)
intsCols = 8:13
metaCols = 1:7
m_logInts = make_intencities(mm_peptides, intsCols)
m_prot.info = make_meta(mm_peptides, metaCols)
m_logInts = convert_log2(m_logInts)
my.pi = eigen_pi(m_logInts, toplot=TRUE)
|
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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