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I searched for how to find feature weights and found this stackoverflow answer. It gives the following equation to get the weights:

w = t(model$coefs) %*% model$SV

The answer mentions that the method is for SVM with linear kernel. Can I use the same equation for radial? Why or Why not?

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No you cannot, because for the RBF kernel $\mathbf{w}$ can never be computed explicitly since it is infinite dimensional.

$\mathbf{w}$ is the separating hyperplane in feature space, which for the linear kernel happens to be input space. The only thing you can compute is an inner product between $\mathbf{w}$ and some test instance in feature space via the kernel trick.

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