sigest {kernlab} | R Documentation

## Hyperparameter estimation for the Gaussian Radial Basis kernel

### Description

Given a range of values for the "sigma" inverse width parameter in the Gaussian Radial Basis kernel for use with Support Vector Machines. The estimation is based on the data to be used.

### Usage

    ## S4 method for signature 'formula':
    sigest(x, data=NULL, frac = 0.25, na.action = na.omit, scaled = TRUE)
    ## S4 method for signature 'matrix':
    sigest(x, frac = 0.25, scaled = TRUE, na.action = na.omit)

### Arguments

`x` | a symbolic description of the model upon the estimation is based. When not using a formula x is a matrix or vector containing the data
`data` | an optional data frame containing the variables in the model. By default the variables are taken from the environment which `ksvm' is called from.
`frac` | Fraction of data to use for estimation. By default a quarter of the data is used to estimate the range of the sigma hyperparameter.
`scaled` | A logical vector indicating the variables to be scaled. If `scaled` is of length 1, the value is recycled as many times as needed and all non-binary variables are scaled. Per default, data are scaled internally to zero mean and unit variance (since this the default action in `ksvm` as well). The center and scale values are returned and used for later predictions.
`na.action` | A function to specify the action to be taken if `NA`s are found. The default action is `na.omit`, which leads to rejection of cases with missing values on any required variable. An alternative is `na.fail`, which causes an error if `NA` cases are found. (NOTE: If given, this argument must be named.)

### Details

`sigest` estimates the range of values for the sigma parameter which would return good results when used with a Support Vector Machine (`ksvm`). The estimation is based upon the 0.1 and 0.9 quantile of |x -x'|^2. Basicly any value in between those two bounds will produce good results.

### Value

Returns a vector of length 2 defining the range (upper bound and lower bound) of the sigma hyperparameter.

### Author(s)

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

### References

B. Caputo, K. Sim, F. Furesjo, A. Smola,
Appearance-based object recognition using SVMs: which kernel should I use?
Proc of NIPS workshop on Statitsical methods for computational experiments in visual processing and computer vision, Whistler, 2002.

### See Also

`ksvm`

### Examples

    ## estimate good sigma values for promotergene
    data(promotergene)
    srange <- sigest(Class~.,data = promotergene)
    srange

    s <- sum(srange)/2
    s
    ## create test and training set
    ind <- sample(1:dim(promotergene)[1],20)
    genetrain <- promotergene[-ind, ]
    genetest <- promotergene[ind, ]

    ## train a support vector machine
    gene <- ksvm(Class~.,data=genetrain,kernel="rbfdot",kpar=list(sigma = s),C=50,cross=3)
    gene

    ## predict gene type on the test set
    promoter <- predict(gene,genetest[,-1])

    ## Check results
    table(promoter,genetest[,1])

[Package kernlab version 0.8-1 Index]