[Traminer-users] quantitative explanatory variables?

[Zuluaga, Juan]

Mr. Studer, thank you very much for the code, it makes sense.
However, are you implying that this is an open question?
The fact that you are seem to be satisfied with MJ Anderson approach for categorical explanatory variables and have implement it in dissassoc(), while you have no equivalent routine for quantitative, does is it mean that you are not satisfied with existing approaches for quantitative explanatory variables?  May I ask you what have you considered (and perhaps rejected)?
-j

From: traminer-users-bounces at r-forge.wu-wien.ac.at [mailto:traminer-users-bounces at r-forge.wu-wien.ac.at] On Behalf Of Matthias Studer
Sent: Monday, October 31, 2011 2:57 AM
To: Users questions
Subject: Re: [Traminer-users] quantitative explanatory variables?

Dear Juan Zuluaga,

I agree with you. Our example dataset lacks an example with a quantitative covariate.

There are two solutions to analyse the link with a quantitative covariate. The first one is to discretize the variable before using it (an example is given below). The second solution is to use the tree procedure. This procedure automatically finds the best cutting points by testing all possible binary splits.  This will also work with ordinal covariates.

An example of both solutions is given below using the biofam dataset (Swiss family life sequences between 15 and 30 years old).

## Loading TraMineR
library(TraMineR)
## Loading the biofam dataset
data(biofam)

## States labels
bf.labels <- c("Parent", "Left", "Married", "Left/Married",  "Child",
                "Left/Child", "Left/Married/Child", "Divorced")
## States short labels for the sequences
bf.shortlab <- c("P","L","M","LM","C","LC", "LMC", "D")
## Building the sequence object
biofam.seq <- seqdef(biofam[,10:25], states=bf.shortlab, labels=bf.labels)
## Computing distance using Optimal matching with transition based substitution costs.
biodist <- seqdist(biofam.seq, method="OM", sm="TRATE", indel=1)

## First solution : Use a discretized variable
## The "cut" function creates a factor using the given cutting points
biofam$cohort <- cut(biofam$birthyr, c(1900, 1930, 1940, 1950, 1960), right=FALSE,
                    labels=c("1900-1929", "1930-1939", "1940-1949", "1950-1959"))
## Compute the association with this new variable
da <- dissassoc(biodist, biofam$cohort, R=1000)
## Printing results
## Differences are highly significant
print(da)


## Second solution : Use the tree procedure
## It will automatically find the best binary splits
biotree <- seqtree(biofam.seq~birthyr, data=biofam, diss=biodist)

##Printing the tree
print(biotree)
## Displaying the tree (adjusting legend fontsize otherwise it's too big)
## You will need to install GraphViz for this
seqtreedisplay(biotree, type="d", legend.fontsize=2)


## Creating a new cohort covariate according to the splitting points found with the tree procedure
biofam$cohort2 <- cut(biofam$birthyr, c(1900, 1929, 1941, 1947, 1951, 1970), right=FALSE,
                    labels=c("<=1928", "1929-1940", "1941-1946", "1947-1950", "1951+"))

## Computing association with this new variable
da2 <- dissassoc(biodist, biofam$cohort2, R=1000)
## Printing results
## Pseudo R2 is slightly higher than before
print(da2)

Hope this helps.

Matthias Studer



Le 30.10.2011 02:04, Zuluaga, Juan a écrit :
Hello Traminer people,
I read your Sociological Methods and Research paper.  The McVicar and Anyadike-Danes (2002) dataset that you used has categorical covariates.
How do you deal with quantitative variates?
Thank you!

-juan zuluaga






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