# scadsvc

##### Fit SCAD SVM model

SVM with variable selection (clone selection) using SCAD penalty.

##### Usage

```
scadsvc(lambda1 = 0.01, x, y, a = 3.7, tol= 10^(-4), class.weights= NULL,
seed=123, maxIter=700, verbose=TRUE)
```

##### Arguments

- lambda1
tuning parameter in SCAD function (default : 0.01)

- x
n-by-d data matrix to train (n chips/patients, d clones/genes)

- y
vector of class labels -1 or 1\'s (for n chips/patiens )

- a
tuning parameter in scad function (default: 3.7)

- tol
the cut-off value to be taken as 0

- class.weights
a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. (default: NULL)

- seed
seed

- maxIter
maximal iteration, default: 700

- verbose
verbose, default: TRUE

##### Details

Adopted from Matlab code: http://www4.stat.ncsu.edu/~hzhang/software.html

##### Value

coefficients of the hyperplane.

intercept of the hyperplane.

the index of the selected features (genes) in the data matrix.

internal calculations product \(xqx = 0.5 * x1 * inv_Q * t(x1)\), see code for more details.

fit of hyperplane f(x) for all _training_ samples with reduced set of features.

the index of the resulting support vectors in the data matrix.

type of svm, from svm function.

optimal lambda1.

corresponding gacv.

nuber of iterations.

##### References

Zhang, H. H., Ahn, J., Lin, X. and Park, C. (2006). * Gene selection using
support vector machines with nonconvex penalty. Bioinformatics, 22, pp. 88-95*.

##### See Also

##### Examples

```
# NOT RUN {
# simulate data
train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=12)
print(str(train))
# train data
model <- scadsvc(as.matrix(t(train$x)), y=train$y, lambda=0.01)
print(str(model))
print(model)
# }
```

*Documentation reproduced from package penalizedSVM, version 1.1.2, License: GPL (>= 2)*