Which are the appropriate prameters for lda modeling?
The main and by far most impactful parameter that you have to search for is k - the number of topics you assume. Conceptually, this is similar to K Nearest Neighbors, in which you have to pre-specify the number of clusters you are searching for and then determine the best fit clusters by properties of the clustering results.

The other parameters, including the method of solving the mathematical problem may produce slightly different results, but none will be nearly as impactful as k.

On a more general note, you can almost always find the answer to this question for an implemented algorithm you're trying to use from the API documentation of the package in question- the topicmodels documentation containing the LDA interface can be found here. 

Often, if you don't well understand the properties of the algorithm that you're trying to use, the documentation will also point you to resources which thoroughly describe the algorithm in question. It is this understanding of the algorithm and what it is doing that will enable you to answer the questions like "What parameters should I tune, over what values, and what is likely to change about the outputs when I do so?"