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class:vsn vsn-class [,vsn-method dim,vsn-method nrow,vsn-method ncol,vsn-method show,vsn-method exprs,vsn-method coef,vsn-method coefficients,vsn-method
- Class to contain result of a vsn fit
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class:vsnInput vsnInput vsnInput-class [,vsnInput-method dim,vsnInput-method nrow,vsnInput-method ncol,vsnInput-method show,vsnInput-method
- Class to contain input data and parameters for vsn functions
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justvsn() vsnrma()
- Wrapper functions for vsn
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kidney
- Intensity data for one cDNA slide with two adjacent tissue samples from a nephrectomy (kidney)
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lymphoma
- Intensity data for 8 cDNA slides with CLL and DLBL samples from the Alizadeh et al. paper in Nature 2000
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meanSdPlot()
- Plot row standard deviations versus row means
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normalize.AffyBatch.vsn()
- Wrapper for vsn to be used as a normalization method with expresso
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sagmbSimulateData() sagmbAssess()
- Simulate data and assess vsn's parameter estimation
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scalingFactorTransformation()
- The transformation that is applied to the scaling parameter of the vsn model
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vsn-package
- vsn
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vsnMatrix() vsn2(<ExpressionSet>) vsn2(<AffyBatch>) vsn2(<NChannelSet>) vsn2(<RGList>)
- Fit the vsn model
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predict(<vsn>)
- Apply the vsn transformation to data
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logLik(<vsnInput>) plotVsnLogLik()
- Calculate the log likelihood and its gradient for the vsn model
