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All functions

BIC(<MArrayLM>)
BIC from model fit
BIC(<MArrayLM2>)
BIC from model fit
ESS()
Effective sample size
MArrayLM2-class
Class MArrayLM2
VarParCIList-class
Class VarParCIList
VarParFitList-class
Class VarParFitList
applyQualityWeights()
Apply pre-specified sample weights
as.data.frame(<varPartResults>)
Convert to data.frame
as.matrix(<varPartResults>)
Convert to matrix
augmentPriorCount()
Augment observed read counts with prior count
calcVarPart()
Compute variance statistics
canCorPairs()
canCorPairs
classifyTestsF(<MArrayLM2>)
Multiple Testing Genewise Across Contrasts
classifyTestsF()
Multiple Testing Genewise Across Contrasts
colinearityScore()
Collinearity score
deviation()
Deviation from expectation for each observation
diffVar()
Test differential variance
dream()
Differential expression with linear mixed model
dscchisq()
Scaled chi-square
eBayes(<MArrayLM2>)
eBayes for MArrayLM2
extractVarPart()
Extract variance statistics
fitExtractVarPartModel()
Fit linear (mixed) model, report variance fractions
fitVarPartModel()
Fit linear (mixed) model
getContrast()
Extract contrast matrix for linear mixed model
getTreat()
Test if coefficient is different from a specified value
get_prediction()
Compute predicted value of formula for linear (mixed) model
ggColorHue()
Default colors for ggplot
hatvalues(<MArrayLM>) hatvalues(<MArrayLM2>)
Compute hatvalues
isRunableFormula()
Test if formula is full rank on this dataset
logLik(<MArrayLM>)
Log-likelihood from model fit
logLik(<MArrayLM2>)
Log-likelihood from model fit
makeContrastsDream()
Construct Matrix of Custom Contrasts
mvTest()
Multivariate tests on results from dream()
mvTest_input-class
Class mvTest_input
plotCompareP()
Compare p-values from two analyses
plotContrasts()
Plot representation of contrast matrix
plotCorrMatrix()
plotCorrMatrix
plotCorrStructure()
plotCorrStructure
plotPercentBars()
Bar plot of gene fractions
plotStratify()
plotStratify
plotStratifyBy()
plotStratifyBy
plotVarPart()
Violin plot of variance fractions
plotVarianceEstimates()
Plot Variance Estimates
rdf()
Residual degrees of freedom
rdf.merMod()
Approximate residual degrees of freedom
rdf_from_matrices()
Fast approximate residual degrees of freedom
reOnly()
Adapted from lme4:::reOnly
residuals(<MArrayLM>)
residuals for MArrayLM
residuals(<MArrayLM2>)
residuals for MArrayLM2
residuals(<VarParFitList>)
Residuals from model fit
residuals.MArrayLM2()
Residuals for result of dream
shrinkageMetric()
Shrinkage metric for eBayes
sortCols()
Sort variance partition statistics
topTable()
Table of Top Genes from Linear Model Fit
varParFrac-class
Class varParFrac
varPartConfInf()
Linear mixed model confidence intervals
varPartData
A simulated dataset of gene counts
varPartData
Simulation dataset for examples
varPartResults-class
Class varPartResults
vcov(<MArrayLM>)
Co-variance matrix for dream() fit
vcov(<MArrayLM2>)
Co-variance matrix for dream() fit
vcovSqrt()
Sqrt of co-variance matrix for dream() fit
voomWithDreamWeights()
Transform RNA-Seq Data Ready for Linear Mixed Modelling with dream()