I am new to R and getting stuck on some points. I've tried posting in other communities and have trawled through the literature, but am finding conflicting texts and opinions. Any additional insight here would be really helpful.
I started off wanting to carry out ordinal logistic regression. I have an ordinal response variable with 3 levels, and ~15 IVs, most of which are categorical. Since 2 of my IVs violate the proportional odds assumption, I decided to use vglm() in the VGAM package, which allows the assumption to be relaxed for specified variables. I have also tried out clm() from the ordinal package. My first problem is that the coefficients produced from either package possess opposite signs. I can see that this can be addressed with the 'reverse=TRUE' argument in vglm(). However, I can't work out what this argument actually means - can anyone shed some light on this? How do I determine which way round the coefficient signs should be?
Further to that, I am aware that classification trees may be an alternative route to go down. Does the proportional odds/parallel lines assumption come into this? Why would you choose to use a decision tree regression over ordinal logistic regression?
Thanks in advance!