Package index
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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()