[Traminer-users] how is NA treated in traminer

[Philip Parker]

Ah of course! Silly me.

Ok second question if that is alright. What would be the typical 
approach to dealing with missing states in sequence analysis. My initial 
feeling is to impute, however, I am not sure how one would combine the 
results gained from traminer using  multiple imputation.

Alternatively should one run tramine with something like a na.omit 
data.frame and then impute transition clusters for further analysis. 
This strategy would seems to introduce considerable bias and thus I 
think is somewhat pointless.

Regards,

Phil

On 20.04.2011 11:32, Gilbert Ritschard wrote:
> Hi Phil,
>
> The seqdplot function (seqplot with the type="d" argument) calls the 
> seqstatd function 
> (http://mephisto.unige.ch/traminer/doc/seqstatd.html) in which the 
> with.missing argument is, by default, set as FALSE.
>
> The solution to display the missings in the sequence distribution plot 
> is to add with.missing=TRUE to the argument list of seqdplot:
>
> seqdplot(seqdata, with.missing=TRUE)
>
> Hope this helps.
> Gilbert
>
>
> On 20-Apr-11 10:50, Philip Parker wrote:
>> Hi All,
>>
>> I am looking at using Traminer to explore post-school transition 
>> pathways. I would like to identify some theoretical typical 
>> transitions (straight to university, gapyear, vocational training, 
>> uncertain (or frequent change) ). I would then like to use these 
>> derived clusters taken from traminer and look at what variables at 
>> school predict entery into these different pathways.
>>
>> So far so good however my data has about 15% missing data (coded as 
>> left=NA, gaps=NA, right=NA). I note that when I run traminer plots 
>> after clustering (and before), the state distribution plots give 
>> group transitions without missing data but the Sequence frequency 
>> plots show the missing data.
>>
>> I was wondering whether traminer imputes the most likely transition 
>> pattern for the missing data and hence the state distribution plots 
>> are based on some sort of imputed full sample, or whether the state 
>> distribution plots represent some sort of listwise deletion by 
>> ploting only complete cases? This becomes important after clustering 
>> as my hypothesised four group turn up. The problem is that the fourth 
>> cluster represents the uncertain (or frequent change) in the 
>> distribution plots but the state  frequency plots suggest that the 
>> group consists largely of people with missing data holes.
>>
>> Regards,
>>
>> Phil
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>>
>