I am running a multinomial logistic regression model in R (using the
multinom function from the
nnet package) with a set of 12 predictor variables. Some of these variables measure the time duration from a reference point until the occurrence of a particular event in experimental trials. In instances where the specified event did not occur, an
na has been entered into the table. This affects a significant number of observations.
My understanding is that
na values should be appropriately treated prior to running a regression model. But in this case it is not clear to me how best to handle the "missing" data. Setting them to zero implies that the event happened instantaneously, when in reality it has not happened at all. Replacing
na values with mean/median is also unsatisfactory, as the model should capture non-occurrences of specific events. Removing these observations from the model excludes too many data points.
What would be the "best" way to proceed in this case?