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