Robust statistical inference for quantitative LC-MS proteomics


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Documentation for package ‘msqrob2’ version 1.13.0

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.StatModel The StatModel class for msqrob
data Example data for 100 proteins
getCoef Accessor functions for StatModel class
getCoef-method Accessor functions for StatModel class
getContrast Methods for StatModel class
getContrast-method Methods for StatModel class
getDF Accessor functions for StatModel class
getDF-method Accessor functions for StatModel class
getDfPosterior Accessor functions for StatModel class
getDfPosterior-method Accessor functions for StatModel class
getFitMethod Accessor functions for StatModel class
getFitMethod-method Accessor functions for StatModel class
getModel Accessor functions for StatModel class
getModel-method Accessor functions for StatModel class
getSigma Accessor functions for StatModel class
getSigma-method Accessor functions for StatModel class
getSigmaPosterior Accessor functions for StatModel class
getSigmaPosterior-method Accessor functions for StatModel class
getVar Accessor functions for StatModel class
getVar-method Accessor functions for StatModel class
getVarPosterior Accessor functions for StatModel class
getVarPosterior-method Accessor functions for StatModel class
getVcovUnscaled Accessor functions for StatModel class
getVcovUnscaled-method Accessor functions for StatModel class
hypothesisTest Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTest-method Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTestHurdle Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTestHurdle-method Parameter estimates, standard errors and statistical inference on differential expression analysis
makeContrast Make contrast matrix
msqrob Methods to fit msqrob models with ridge regression and/or random effects using lme4
msqrob-method Methods to fit msqrob models with ridge regression and/or random effects using lme4
msqrobAggregate Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.
msqrobAggregate-method Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.
msqrobGlm Function to fit msqrob models to peptide counts using glm
msqrobHurdle Function to fit msqrob hurdle models
msqrobHurdle-method Function to fit msqrob hurdle models
msqrobLm Function to fit msqrob models using lm and rlm
msqrobLmer Function to fit msqrob models with ridge regression and/or random effects using lme4
msqrobQB Function to fit msqrob models to peptide counts using glm
msqrobQB-method Function to fit msqrob models to peptide counts using glm
pe Example data for 100 proteins
show-method The StatModel class for msqrob
smallestUniqueGroups Smallest unique protein groups
StatModel The StatModel class for msqrob
StatModel-class The StatModel class for msqrob
StatModel-method Methods for StatModel class
statModelAccessors Accessor functions for StatModel class
statModelMethods Methods for StatModel class
topFeatures Toplist of DE proteins, peptides or features
varContrast Methods for StatModel class
varContrast-method Methods for StatModel class