I am struggling to understand my Multinominal Logistic Regression. This is my first time ever tackling such a model. Note that I was following this recipe.
I am trying to predict the redemption rate (shareholders that wish to refund their money from their SPAC investment) with the predictors shown below. The dependent variable Y is redemption rate ranges from 0-1, with 1 being really bad and all refunded their money. It is split up in three groups; 1 low redemption rate, 2 middle and 3 high.
I am familiar with OLS and log interpretation but struggle to understand this model.
Could anyone shine in with guidance on the coefficients? Like, for one unit increase in Total Assets, how will that affect the group low redemption rate relative to the middle?
The model was executed in R.
Sector is a dummy for the Healthcare sector and profitability is a dummy for if they were profitable before their merger
nnet
automatically convert them into categories, I don't know why. I was left with over 100 categories if not converting them into 3 categories myself. However it is not an issue, as I just want to predict them in such a way regardless. Despite the loss of information, it still managed to predict with 80.77% accuracy. $\endgroup$nnet::multinom
model is giving you hints not to use it. $\endgroup$