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Hi Judith,<br>
<br>
Question 1: <br>
To get the sequence ids (indexes) for each cluster, you can use for
example:<br>
<br>
which(cluster4=="Cluster 1")<br>
which(cluster4=="Cluster 2")<br>
...<br>
<br>
This will return the indexes of the sequences classified in cluster
1, 2, ....<br>
<br>
Question 2:<br>
Each plot type has a corresponding function that produces the
statistics. In this case, this is the seqmeant() function. See our
paper in the Journal of Statistical Software
(<a class="moz-txt-link-freetext" href="http://www.jstatsoft.org/v40/i04">http://www.jstatsoft.org/v40/i04</a>) and the manual pages.<br>
<br>
All the best,<br>
Alexis<br>
<br>
Le 29. 09. 11 09:50, Judith Krüger a écrit :
<blockquote
cite="mid:CAKv-uxujkROAdckR4zo4fmQxKzx743DidcEPph26n_t9JBP+tQ@mail.gmail.com"
type="cite"><br>
<div class="gmail_quote"><font size="2">Hey everyone,<br>
<br>
I use R and the package TraMineR to analyse payment data in
151 cases.</font> As you might expect, I have some trouble...<br>
<br>
Question 1: I successfully calculated an optimal matching
analysis and a cluster analysis. I also generated the necessary
graphs.<b> How to I get the IDs into the graphs</b> OR <b>How
can I get a table of every cluster and the relevant sequences?</b>
<br>
<br>
Question 2: E.g., when plotting the mean times spent in each
state per cluster (seqmtplot) oder other graphs, <b>how can I
get a table with the corresponding values </b>(since I can
only guess the values in the graphs)<b>?</b> <br>
<br>
Thanks a lot in advance and good luck with all your projects!<br>
<br>
Greetz<br>
Judie<br>
<br>
<font size="1"><span style="color: rgb(204, 0, 0);">>
library(TraMineR)<br>
> library(foreign)<br>
> library(cluster)<br>
> library(RColorBrewer)<br>
> datenR <-
read.spss("Y:\\DOKTORARBEIT\\1_Dissertation\\3_Methode\\2_Erwerbsverläufe\\5_R\\datenR.sav",
to.data.frame=TRUE, use.value.labels=FALSE)<br>
> datenR.labels <- c("ES6", "ES7", "ES8", "ES9",
"ES10", "ES11", "ES12", "ES13", "ES14", "Ruhendes
Arb.verh.", "Austritt", "Muttersch., Erz.-Elt.zeit",
"Wehrdienst", "Weiterbildung")</span><br style="color:
rgb(204, 0, 0);">
<span style="color: rgb(204, 0, 0);">> datenR.seq <-
seqdef(datenR, var=20:103, labels=datenR.labels, id="auto")</span><br>
[>] found missing values ('NA') in sequence data<br>
[>] preparing 151 sequences<br>
[>] coding void elements with '%' and missing values with
'*'<br>
[>] 14 distinct states appear in the data: <br>
1 = 6<br>
2 = 7<br>
3 = 8<br>
4 = 9<br>
5 = 10<br>
6 = 11<br>
7 = 12<br>
8 = 13<br>
9 = 14<br>
10 = 44<br>
11 = 55<br>
12 = 66<br>
...<br>
[>] alphabet (state labels): <br>
1 = 6 (ES6)<br>
2 = 7 (ES7)<br>
3 = 8 (ES8)<br>
4 = 9 (ES9)<br>
5 = 10 (ES10)<br>
6 = 11 (ES11)<br>
7 = 12 (ES12)<br>
8 = 13 (ES13)<br>
9 = 14 (ES14)<br>
10 = 44 (Ruhendes Arb.verh.)<br>
11 = 55 (Austritt)<br>
12 = 66 (Muttersch., Erz.-Elt.zeit)<br>
... (14 states)<br>
[>] no color palette attributed, provide one to use
graphical functions<br>
[>] 151 sequences in the data set<br>
[>] min/max sequence length: 2/84<br>
Warnmeldung:<br>
[!] no automatic color palete attributed, number of
states>12. <br>
Use 'cpal' argument to define one. <br>
<span style="color: rgb(204, 0, 0);">> cpal(datenR.seq)
<- c("white", "yellow", "orange", "hotpink", "red1",
"red3", "darkred", "skyblue", "blue", "grey80", "grey60",
"springgreen", "grey20", "purple")</span><br style="color:
rgb(204, 0, 0);">
<span style="color: rgb(204, 0, 0);">> subcostmatrix <-
seqsubm(datenR.seq, method="TRATE")</span><br>
[>] creating substitution-cost matrix using transition
rates ...<br>
[>] computing transition rates for states
6/7/8/9/10/11/12/13/14/44/55/66/77/88 ...<br>
<span style="color: rgb(204, 0, 0);">> round(subcostmatrix,
2)</span><br>
6-> 7-> 8-> 9-> 10-> 11-> 12->
13-> 14-> 44-> 55-> 66-> 77-> 88-><br>
6-> 0.00 1.75 2.00 1.75 2.00 2.00 2.00 2.00 2.00 2.00 2.00
2.00 2.00 2.0<br>
7-> 1.75 0.00 1.89 1.98 1.99 2.00 2.00 2.00 2.00 2.00 <a
moz-do-not-send="true" style="color: rgb(0, 0, 0);"
href="tel:1.93%201.92%201.72%C2%A0%202.0"
value="+19319217220" target="_blank">1.93 1.92 1.72 2.0</a><br
style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">8-> 2.00 1.89 0.00 1.89
1.99 2.00 2.00 2.00 2.00 2.00 1.86 1.88 1.90 1.4</span><br
style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">9-> 1.75 1.98 1.89 0.00
1.82 2.00 2.00 2.00 2.00 1.75 2.00 1.98 1.95 2.0</span><br
style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">
10-> 2.00 1.99 1.99 1.82 0.00 1.85 2.00 2.00 2.00 2.00
2.00 1.99 1.98 2.0</span><br style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">11-> 2.00 2.00 2.00 2.00
1.85 0.00 1.91 2.00 2.00 2.00 2.00 1.90 2.00 2.0</span><br
style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">12-> 2.00 2.00 2.00 2.00
2.00 1.91 0.00 1.99 2.00 2.00 2.00 1.92 2.00 2.0</span><br
style="color: rgb(0, 0, 0);">
<span style="color: rgb(0, 0, 0);">
13-> 2.00 2.00 2.00 2.00 2.00 2.00 1.99 0.00 1.99 2.00
2.00 1.97 2.00 2.0</span><br>
14-> 2.00 2.00 2.00 2.00 2.00 2.00 2.00 1.99 0.00 2.00 2.00
2.00 2.00 2.0<br>
44-> 2.00 2.00 2.00 1.75 2.00 2.00 2.00 2.00 2.00 0.00 2.00
1.75 2.00 2.0<br>
55-> 2.00 1.93 1.86 2.00 2.00 2.00 2.00 2.00 2.00 2.00 0.00
1.98 2.00 2.0<br>
66-> 2.00 1.92 1.88 1.98 1.99 1.90 1.92 1.97 2.00 1.75 1.98
0.00 2.00 2.0<br>
77-> 2.00 1.72 1.90 1.95 1.98 2.00 2.00 2.00 2.00 2.00 2.00
2.00 0.00 2.0<br>
88-> 2.00 2.00 1.40 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00
2.00 2.00 0.0<br>
<span style="color: rgb(204, 0, 0);">> datenR.om <-
seqdist(datenR.seq, method="OM", indel=2, sm=subcostmatrix)</span><br>
[>] 151 sequences with 14 distinct events/states<br>
[>] 147 distinct sequences<br>
[>] min/max sequence length: 2/84<br>
[>] computing distances using OM metric<br>
[>] total time: 0.33 secs<br>
Warnmeldung:<br>
The substitution cost matrix is not symmetric. <br>
<span style="color: rgb(204, 0, 0);">> clusterward <-
agnes(datenR.om, diss=TRUE, method="ward")<br>
> plot(clusterward, which.plots=2)<br>
> cluster4 <- cutree(clusterward, k=4)<br>
> cluster4 <- factor(cluster4, labels=c("Cluster 1",
"Cluster 2", "Cluster 3", "Cluster 4"))<br>
> table(cluster4)</span><br>
cluster4<br>
Cluster 1 Cluster 2 Cluster 3 Cluster 4 <br>
32 17 35 67 <br>
<span style="color: rgb(204, 0, 0);">> seqfplot(datenR.seq,
group=cluster4, pbarw=T, tlim=0, border=NA)</span><br>
<span style="color: rgb(204, 0, 0);">>
seqmtplot(datenR.seq, group=cluster4)</span></font><br>
<font color="#888888"><br>
Judith Krüger<br>
Ph.D. Student<br>
Germany<br>
</font></div>
<br>
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