[Rsiena-help] SIENA: rate function with large s.e.

Wed Sep 5 15:58:54 CEST 2012

Hi Christian

Thank you very much - I see. The conditioning of the decision-making on 
the state Y(t) makes it explicit. I didn't see this clear enough.

Best regards,
Philippe

On 05.09.12 14:20, Christian Steglich wrote:
> Hello Philippe,
>
> 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.
>
> 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.
>
> Best regards,
> Ch
>
> Am 05/09/2012 14:59, schrieb Philippe Sulger:
>> Hi Christian
>>
>> Ok. Thank you very much for your reply. I will follow your suggestions.
>>
>> Btw.: I was/am reading Snijders et al. (2007) 
>> <http://www.stats.ox.ac.uk/%7Esnijders/siena/chapter_coevol.pdf>. I 
>> have already dropped a line to Tom, but I do not want to bother with 
>> (misplaced and unjustfied) pickiness, or redundant details: 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 book 
>> <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> 
>> they are as in the paper...
>>
>> And on page 7: shouldn't the process be a /right/-continuous function 
>> of time? I am sorry about these questions but I just want to make 
>> sure that I understand it correctly.
>>
>>
>> Best,
>> Philippe
>>
>> On 05.09.12 12:39, Christian Steglich wrote:
>>> Hi Philippe,
>>>
>>> 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.
>>>
>>> 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.
>>>
>>> Best,
>>> Ch
>>>
>>>
>>>
>>> Am 14/07/2012 18:06, schrieb Philippe Sulger:
>>>> Dear RSiena-Users
>>>>
>>>> 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:
>>>>
>>>> Estimate   Standard   Convergence
>>>> Error      t-ratio
>>>> Network Dynamics
>>>>    1. rate  basic rate parameter friends           7.8441 (  
>>>> 1.3870  )    0.0131
>>>>    2. eval  outdegree (density)                   -1.1287 (  
>>>> 0.1991  )   -0.0392
>>>>    3. eval  reciprocity                            1.8075 (  
>>>> 1.1237  )    0.0220
>>>>    4. endow reciprocity                           -1.7207 (  
>>>> 1.9371  )   -0.0050
>>>>    5. eval  balance                                0.2603 (  
>>>> 0.0565  )    0.0264
>>>>    6. eval  same covariate                         0.2877 (  
>>>> 0.2220  )    0.0135
>>>>
>>>> Behavior Dynamics
>>>>    7. rate  rate behav period 1                   34.9277 ( 
>>>> 36.6589  )   -0.0177
>>>>    8. eval  behavior behav linear shape           -0.0327 (  
>>>> 0.0592  )   -0.0194
>>>>    9. eval  behavior behav quadratic shape         0.0003 (  
>>>> 0.0048  )   -0.0220
>>>>   10. eval  behavior behav: effect from covariate -0.0096 (  
>>>> 0.1383  )   -0.0588
>>>>
>>>> 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.
>>>>
>>>> Are these large s.e.'s of concern?
>>>>
>>>> Thanks for your help.
>>>>
>>>> Best,
>>>> Philippe
>>>>
>>>>
>>>> _______________________________________________
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>>>
>>>
>>> -- 
>>> _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
>>>
>>> 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
>>>
>>> http://www.gmw.rug.nl/~steglich/
>>> _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
>>>
>>
>
>
> -- 
> _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
>
> 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
>
> http://www.gmw.rug.nl/~steglich/
> _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _
>

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