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<div class="moz-cite-prefix">Hello Philippe,<br>
<br>
you are right about formula 29 in the 2007 book chapter, indeed
the conditions need to be switched to match what is written
(correctly) in the text.<br>
<br>
We wrote 'left-continuous' because we thought until the decision
to change something is actually taken, including this decision
time point, the state remains the same, and it changes in
consequence of the decision, infinitesimally after that time
point. It doesn't actually matter, one could just as well argue
for a right-continuous process, I guess - the difference between
the two is on a null set for the Markov process anyway. But the
left-continuous version makes it more convenient to write down
equations like (9) and (10), depending on the state at the
decision moment.<br>
<br>
Best regards,<br>
Ch<br>
<br>
Am 05/09/2012 14:59, schrieb Philippe Sulger:<br>
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<font face="Courier New, Courier, monospace">Hi Christian<br>
<br>
Ok. Thank you very much for your reply. I will follow your
suggestions.<br>
<br>
Btw.: I was/am reading </font><font face="Courier New, Courier,
monospace"><font face="Courier New, Courier, monospace"><font
face="Courier New, Courier, monospace"><a
moz-do-not-send="true"
href="http://www.stats.ox.ac.uk/%7Esnijders/siena/chapter_coevol.pdf">Snijders
et al. (2007)</a></font></font>. I have already dropped a
line to Tom, but I do not want to bother with (misplaced and
unjustfied) pickiness, or redundant details: </font><font
face="Courier New, Courier, monospace"><font face="Courier New,
Courier, monospace">I certainly miss something/do not
understand something as I should, but up to now, in equation
(29), the "if" - conditions should be switched so that they
are consistent with the previous text making sense to me,
shouldn't they? In any case, I am doubting that my guess is
right, since in the <a moz-do-not-send="true"
href="http://books.google.ch/books?id=RS_tDqROnacC&pg=PA41&lpg=PA41&dq=Longitudinal+models+in+the+behavioral+and+related+sciences&source=bl&ots=aYjhHBuWu7&sig=7paHd8Dp8QnaCyH2xQ2a9DbgZZg&hl=de#v=onepage&q=Longitudinal%20models%20in%20the%20behavioral%20and%20related%20sciences&f=false">book</a>
they are as in the paper...<br>
<br>
And on page 7: shouldn't the process be a <i>right</i>-continuous
function of time? I am sorry about these questions but I just
want to make sure that I understand it correctly.<br>
<br>
<br>
</font>Best,<br>
Philippe<br>
<br>
</font>
<div class="moz-cite-prefix">On 05.09.12 12:39, Christian Steglich
wrote:<br>
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<div class="moz-cite-prefix">Hi Philippe,<br>
<br>
your result suggests that behaviour change is very quick
compared to network change, i.e., that according to the given
model specification (a very sparse one), the first behaviour
measure is actually non-informative for explaining the
evolution to the second behaviour measure.<br>
<br>
This could be due to several things: actor heterogeneity when
the model assumes homogeneity, some other type of model
misspecification, use of an inflated behaviour scale (your
parameters are very small, suggesting as much), or true
independence in your behaviour responses. I'd suggest you
check the micro step distribution first (who makes how many
steps in which direction) for any anomalies.<br>
<br>
Best,<br>
Ch<br>
<br>
<br>
<br>
Am 14/07/2012 18:06, schrieb Philippe Sulger:<br>
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<font face="Courier New, Courier, monospace">Dear RSiena-Users<br>
<br>
</font><font face="Courier New, Courier, monospace">I jointly
analyze network dynamics and behavioral dynamics. Something
that I am concerned about are the large standard errors on
the estimate of the behavioral rate parameter. Here an
example:<br>
<br>
Estimate
Standard Convergence <br>
Error t-ratio <br>
Network Dynamics <br>
1. rate basic rate parameter friends 7.8441
( 1.3870 ) 0.0131 <br>
2. eval outdegree (density) -1.1287
( 0.1991 ) -0.0392 <br>
3. eval reciprocity 1.8075
( 1.1237 ) 0.0220 <br>
4. endow reciprocity -1.7207
( 1.9371 ) -0.0050 <br>
5. eval balance 0.2603
( 0.0565 ) 0.0264 <br>
6. eval same covariate 0.2877
( 0.2220 ) 0.0135 <br>
<br>
Behavior Dynamics<br>
7. rate rate behav period 1 34.9277 (
36.6589 ) -0.0177 <br>
8. eval behavior behav linear shape -0.0327
( 0.0592 ) -0.0194 <br>
9. eval behavior behav quadratic shape 0.0003
( 0.0048 ) -0.0220 <br>
10. eval behavior behav: effect from covariate -0.0096
( 0.1383 ) -0.0588 <br>
<br>
As you see, convergence is good. Even if I exclude the 10th
effect (effect from gender, in many networks I get large
s.e. for the rate on aggressive behavior). I also made the
rate of behav dependent on covariates with no essential
effect.<br>
<br>
Are these large s.e.'s of concern?<br>
<br>
Thanks for your help.<br>
<br>
Best,<br>
Philippe<br>
</font> <br>
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<pre class="moz-signature" cols="72">--
_ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
Christian Steglich, researcher
Faculty of Behavioural and Social Sciences
University of Groningen
Grote Rozenstraat 31
9712 TG Groningen
The Netherlands
fon +31-(0)50-363 6189
fax +31-(0)50-363 6304
<a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://www.gmw.rug.nl/%7Esteglich/">http://www.gmw.rug.nl/~steglich/</a>
_ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
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<br>
<pre class="moz-signature" cols="72">--
_ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
Christian Steglich, researcher
Faculty of Behavioural and Social Sciences
University of Groningen
Grote Rozenstraat 31
9712 TG Groningen
The Netherlands
fon +31-(0)50-363 6189
fax +31-(0)50-363 6304
<a class="moz-txt-link-freetext" href="http://www.gmw.rug.nl/~steglich/">http://www.gmw.rug.nl/~steglich/</a>
_ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
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