We have a question about how to deal with missing values/gaps within sequences. We like to set up our own substitution-cost matrix for the Optimal Matching process. As far as we know, TraMiner allows creating own cost matrices - but only in case there are no missing values. If there are missing values there is a default matrix which 'charges' 2 units for all states regardless of handling with 'real' states or the missing state (cf. TraMiner User Guide, chapter 6.5.2 & 9.4.5).
Background: Right now, we evaluate a life-course dataset with career progression which lasts up to 40 years. Matthias Studer of the TraMiner-Development Team kindly gave us the advice trying the Multiple Imputation Method of Brendan Halpin (see his paper here). As Mr. Halpin pointed out with good reason, it will produce bias tendencies setting missing values as a special category and assigning own substitution cost (as the TraMiner User Guide advises by indicating the problems of this operation, cf. Ibid. chapter 9.4.5). Therefor Multiple Imputation makes sense. Unfortunately we have to deal with much bigger amount of gaps than Brendan Halpin, so it will be good to try both and compare, the substitution and the imputation method. Mr. Halpin confirmed so after having checked our data.
So far, using the default 2-units-for-all-substitution of the substitution method the clustering results shows the frequence distribution/state distribution plots are beeing clustered with tendency to the pattern of the 'missing state'. That means that the cluster types obey the 'missing state' pattern which is no good result. So we like to try other substitution cost substitution for all of the states including the missing state. But how to do so?