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I'm trying to analyse multiple sequences with TraMineR at once. I've had a look at seqdef but I'm struggling to understand how I'd create a TraMineR dataset when I'm dealing with multiple variables. I guess I'm working with something similar to the dataset used by Aassve et al. (as mentioned in the tutorial), whereby each wave has information about several states (e.g. children, marriage, employment). All my variables are binary. Here's an example of a dataset with three waves (D,W2,W3) and three variables.

D<-data.frame(ID=c(1:4),A1=c(1,1,1,0),B1=c(0,1,0,1),C1=c(0,0,0,1))
W2<-data.frame(A2=c(0,1,1,0),B2=c(1,1,0,1),C2=c(0,1,0,1))
W3<-data.frame(A3=c(0,1,1,0),B3=c(1,1,0,1),C3=c(0,1,0,1))
L<-data.frame(D,W2,W3)

I may be wrong but the material I found deals with the data management and analysis of one variable at a time only (e.g. employment status across several waves). My dataset is much larger than the above so I can't really impute these manually as shown on page 48 of the tutorial. Has anyone dealt with this type of data using TraMineR (or similar package)?

1) How would you feed the data above to TraMineR?

2) How would you compute the substitution costs and then cluster them?

Many thanks

maycobra
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2 Answers2

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When using sequence analysis, we are interested in the evolution of one variable (for instance, a sequence of one variable across several waves). You have then multiple possibilities to analyze several variables:

  • Create on sequences per variable and then analyze the links between the cluster of sequences. In my opinion, this is the best way to go, if your variables measure different concepts (for instance, family and employment).
  • Create a new variable for each wave that is the interaction of the different variables of one wave using the interaction function. For instance, for wave one, use L$IntVar1 <- interaction(L$A1, L$B1, L$C1, drop=T) (use drop=T to remove unused combination of answers). And then analyze the sequence of this newly created variable. In my opinion, this is the prefered way if your variables are different dimensions of the same concept. For instance, marriage, children and union are all related to familly life.
  • Create one sequence object per variable and then use seqdistmc to compute the distance (multi-channel sequence analysis). This is equivalent to the previous method depending on how you will set substitution costs (see below).

If you use the second strategy, you could use the following substitution costs. You can count the differences between the original variable to set the substition costs. For instance, between states "Married, Child" and "Not married and Child", you could set the substitution to "1" because there is only a difference on the "marriage" variable. Similarly, you would set the substition cost between states "Married, Child" and "Not married and No Child" to "2" because all of your variables are different. Finally, you set the indel cost to half the maximum substitution cost. This is the strategy used by seqdistmc.

Hope this helps.

Matthias Studer
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In Biemann and Datta (2013) they talk about multi dimensional analysis. That means creating multiple sequences for the same "individuals".

I used the following approach to do so:

1) define 3 dimensional sequences

comp.seq <- seqdef(comp,NULL,states=comp.scodes,labels=comp.labels, alphabet=comp.alphabet,missing="Z")
titles.seq <- seqdef(titles,NULL,states=titles.scodes,labels=titles.labels, alphabet=titles.alphabet,missing="Z")
member.seq <- seqdef(member,NULL,states=member.scodes,labels=member.labels, alphabet=member.alphabet,missing="Z")

2) Compute the multi channel (multi dimension) distance

mcdist <- seqdistmc(channels=list(comp.seq,member.seq,titles.seq),method="OM",sm=list("TRATE","TRATE","TRATE"),with.missing=TRUE)

3) cluster it with ward's method:

library(cluster)
clusterward<- agnes(mcdist,diss=TRUE,method="ward")
plot(clusterward,which.plots=2)

Nevermind the parameters like "missing" or "left" and etc. but i hope the brief code sample helps.

Pedro Braz
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