# sigest

##### Hyperparameter estimation for the Gaussian Radial Basis kernel

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.

- Keywords
- regression, classif

##### Usage

```
# S4 method for formula
sigest(x, data=NULL, frac = 0.5, na.action = na.omit, scaled = TRUE)
# S4 method for matrix
sigest(x, frac = 0.5, 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 \(\|x -x'\|^2\). Basically any value in between those two bounds will
produce good results.

##### Value

Returns a vector of length 3 defining the range (0.1 quantile, median and 0.9 quantile) of the sigma hyperparameter.

##### 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

##### Examples

```
# NOT RUN {
## estimate good sigma values for promotergene
data(promotergene)
srange <- sigest(Class~.,data = promotergene)
srange
s <- 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])
# }
```

*Documentation reproduced from package kernlab, version 0.9-29, License:*