evaluateParameters: Evaluate Performance of Kernel Parameters by Cross-validation
In macat: MicroArray Chromosome Analysis Tool

Description Usage Arguments Value Author(s) See Also Examples

For a given data set, chromosome, class, and kernel function, this function helps in determining optimal settings for the kernel parameter(s). The performance of individual parameter setting is assessed by cross- validation.

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evaluateParameters(data, class, chromosome, kernel, kernelparams = NULL,
                   paramMultipliers = 2^(-4:4), subset = NULL, 
                   newlabels = NULL, ncross = 10, verbose = TRUE)

`data`	Gene expression data in the MACAT list format. See data(stjude) for an example.
`class`	Sample class to be analyzed
`chromosome`	Chromosome to be analyzed
`kernel`	Choose kernel to smooth scores along the chromosome. Available are 'kNN' for k-Nearest-Neighbors, 'rbf' for radial-basis-function (Gaussian), 'basePairDistance' for a kernel, which averages over all genes within a given range of base pairs around a position.
`kernelparams`	Additional parameters for the kernel as list, e.g., kernelparams=list(k=5) for taking the 5 nearest neighbours in the kNN-kernel. If NULL some defaults are set within the function.
`paramMultipliers`	Numeric vector. If you do cross-validation of the kernel parameters, specify these as multipliers of the given (standard) kernel parameter, depending on your kernel choice (see page 5 of the vignette). The multiplication results are the kernel argument settings, among which you want to search for the optimal one using cross-validation.
`subset`	If a subset of samples is to be used, give vector of column- indices of these samples in the original matrix here.
`newlabels`	If other labels than the ones in the MACAT-list-structure are to be used, give them as character vector/factor here. Make sure argument 'class' is one of them.
`ncross`	Integer. Specify how many folds in cross-validation.
`verbose`	Logical. Should progress be reported to STDOUT?

A list of class 'MACATevP' with 4 components:

`[parameterName]`	List of assessed settings for the parameter [parameterName].
`avgResid`	Average Residual Sum of Squares for the parameter settings in the same order as the first component.
`multiplier`	Multiplier of the original parameters in the same order as the first components.
`best`	List of parameter settings considered optimal by cross- validation. Can be directly inserted under the argument 'kernelparams' of the 'evalScoring' function.

MACAT development team

evalScoring

data(stjd)
evalkNN6 <- evaluateParameters(stjd, class="T", chromosome=6,kernel=kNN, 
                               paramMultipliers=c(0.01,seq(0.2,2.0,0.2),2.5))
if (interactive()&&capabilities("X11"))
  plot(evalkNN6)