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Dear Juan Zuluaga,<br>
<br>
I agree with you. Our example dataset lacks an example with a
quantitative covariate. <br>
<br>
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.<br>
<br>
An example of both solutions is given below using the biofam dataset
(Swiss family life sequences between 15 and 30 years old).<br>
<br>
## Loading TraMineR<br>
library(TraMineR)<br>
## Loading the biofam dataset<br>
data(biofam)<br>
<br>
## States labels<br>
bf.labels <- c("Parent", "Left", "Married", "Left/Married",
"Child", <br>
"Left/Child", "Left/Married/Child", "Divorced")<br>
## States short labels for the sequences<br>
bf.shortlab <- c("P","L","M","LM","C","LC", "LMC", "D")<br>
## Building the sequence object<br>
biofam.seq <- seqdef(biofam[,10:25], states=bf.shortlab,
labels=bf.labels)<br>
## Computing distance using Optimal matching with transition based
substitution costs.<br>
biodist <- seqdist(biofam.seq, method="OM", sm="TRATE", indel=1)<br>
<br>
## First solution : Use a discretized variable<br>
## The "cut" function creates a factor using the given cutting
points<br>
biofam$cohort <- cut(biofam$birthyr, c(1900, 1930, 1940, 1950,
1960), right=FALSE, <br>
labels=c("1900-1929", "1930-1939", "1940-1949",
"1950-1959"))<br>
## Compute the association with this new variable<br>
da <- dissassoc(biodist, biofam$cohort, R=1000)<br>
## Printing results<br>
## Differences are highly significant <br>
print(da)<br>
<br>
<br>
## Second solution : Use the tree procedure<br>
## It will automatically find the best binary splits<br>
biotree <- seqtree(biofam.seq~birthyr, data=biofam, diss=biodist)<br>
<br>
##Printing the tree<br>
print(biotree)<br>
## Displaying the tree (adjusting legend fontsize otherwise it's too
big)<br>
## You will need to install GraphViz for this<br>
seqtreedisplay(biotree, type="d", legend.fontsize=2)<br>
<br>
<br>
## Creating a new cohort covariate according to the splitting points
found with the tree procedure<br>
biofam$cohort2 <- cut(biofam$birthyr, c(1900, 1929, 1941, 1947,
1951, 1970), right=FALSE, <br>
labels=c("<=1928", "1929-1940", "1941-1946",
"1947-1950", "1951+"))<br>
<br>
## Computing association with this new variable<br>
da2 <- dissassoc(biodist, biofam$cohort2, R=1000)<br>
## Printing results<br>
## Pseudo R2 is slightly higher than before<br>
print(da2)<br>
<br>
Hope this helps.<br>
<br>
Matthias Studer<br>
<br>
<br>
<br>
Le 30.10.2011 02:04, Zuluaga, Juan a écrit :
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<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Hello
Traminer people, <o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">I
read your Sociological Methods and Research paper. The
McVicar and Anyadike-Danes (2002) dataset that you used has
categorical covariates. <o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">How
do you deal with quantitative variates? <o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Thank
you!<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">-juan
zuluaga<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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