Crossvalidation for linear support vector machine

Hi

I have the following code which performs k-fold crossvalidation. And as you can see the kernel chosen is a linear kernel. However, what I want to do is to perform gridsearch for the parameter C - how is that done in Matlab? Would you use standard gridsearch method as an outer loop on the k-fold crossvalidation or is there a gridsearch function specific for SVMs (I did not find any). Does anyone have me example code?

This is my code so far:

load feature
load class

k = 4; %number of folds
cvFolds = crossvalind('Kfold', class, k);
cp = classperf(class);

for i = 1:k                                  %# for each fold
    testIdx = (cvFolds == i);                %# get indices of test instances
    trainIdx = ~testIdx;                     %# get indices training instances

      %# train an SVM model for autistic vs TD, k-fold crossvalidatio
      svmModel = svmtrain(feature(trainIdx,:), class(trainIdx), ...
                   'Autoscale',true, 'Showplot',false, 'Method','QP', ...
                   'BoxConstraint',0.2, 'Kernel_Function','linear');

      %# test using test instances
      pred = svmclassify(svmModel, feature(testIdx,:), 'Showplot',false);

      %# evaluate and update performance object
      cp = classperf(cp, pred, testIdx);
  end

%# get accuracy
cp.CorrectRate

%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix