[Traminer-users] dissmfac coefficients

[Alexandre Pollien]

Thank you,my doubts are lifted
Alexandre




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> Today's Topics:
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>    1. dissmfac coefficients (Alexandre Pollien)
>    2. Re: dissmfac coefficients (Matthias Studer)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Tue, 24 Aug 2010 10:16:24 +0200
> From: Alexandre Pollien <alexandre.pollien at bluewin.ch>
> Subject: [Traminer-users] dissmfac coefficients
> To: traminer-users at lists.r-forge.r-project.org
> Message-ID: <4C737FD8.6010309 at bluewin.ch>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
> Hello
>
> I have a question about the interpretation of the dissmfac function. I 
> hope this is not stupid and understable.
>
> Can the pseudo-F, pseudo-R2 be interpreted and used in the same way that 
> regression coefficients? In other words, should we pay attention to 
> problems of collinearity? Is this really multifactorial system or simply 
> a series of independent coefficients?
> I ask these questions because I did an analysis with strongly correlated 
> variables, and the results do not seem bad:
> I would like to determine the breakpoint of an experience effect : exper 
> = 1 to 8 and I recode it like this:
>
> exper1 (=1 / 2->8)
> exper2 (= 1->2 / 3->8)
> exper3 (= 1->3 / 4->8)
> exper4 (= 1->4 / 5->8)
> etc. ...
>
> I suspect that the experience effect is very strong at first, then 
> gradually decreases. The results are:
>
> PseudoR2:
> 7.49E-02
> 4.57E-02
> 3.68E-02
> 9.54E-03
> 7.49E-03
> 7.44E-03
> 4.49E-03
>
> The passage from one experience to two produced a change, as well as 2 
> to 3, 3 to 4. But a break seems to occur at the 4th level of experience. 
> Having 4 or having 5 (or 6,7 etc..) units experience seems to make not 
> very difference (even if the 4th p-value = 0.000). Is that right?
>
> Thank a lot
>
> Alexandre
>
>
> ------------------------------
>
> Message: 2
> Date: Thu, 26 Aug 2010 15:21:13 +0200
> From: Matthias Studer <Matthias.Studer at unige.ch>
> Subject: Re: [Traminer-users] dissmfac coefficients
> To: Users questions <traminer-users at lists.r-forge.r-project.org>
> Message-ID: <4C766A49.7020007 at unige.ch>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
>
> Hello,
>
> As in regression, you should not experience too much collinearity 
> problems. However, in case of strong multicollinearity, you may 
> experience computational problem (as in multiple regression).
>
> Please consider the following example:
> ## Defining a state sequence object
> data(mvad)
> mvad.seq <- seqdef(mvad[, 17:86])
>
> ## Building dissimilarities
> mvad.lcs <- seqdist(mvad.seq, method="LCS")
> print(dissmfac(mvad.lcs ~ Grammar +    gcse5eq, data=mvad, R=1000))
>
> The result wil be:
>
>    Variable  PseudoF   PseudoR2 p_value
> 1  Grammar 21.86726 0.02595961       0
> 2  gcse5eq 90.87825 0.10788564       0
> 3    Total 67.27347 0.15950143       0
>
>
> The "total " pseudo-R2 (last line) tells us that the full model 
> "explain" around 16% of the discrepancy of the sequence.
>
> The pseudo-R2 computed for each covariate can be interpreted as the loss 
> of explanatory power if this covariate is removed from the full model. 
> If we remove the gcse5eq covariate, the R2 of the model decreases by 0.108.
>
> A more in depth presentation (as well as formulae) may be found in this 
> article (section 5): 
> http://mephisto.unige.ch/pub/publications/gr/Studer_akdm_2010.pdf
>
> Hence, in your case, the pseudo R2 of 3.68E-02 for exper3 tells you that 
> the R2 of your model will decreases by 3.68E-02 if your remove the 
> exper3 covariate (but keep exper1, exper2, exper4, expert5...). exper7 
> is significant, hence it add a valuable information to the full model 
> (although less than expert1).
>
> Did I answer your question ?
>
> Matthias
>
> Le 24.08.2010 10:16, Alexandre Pollien a ?crit :
>   
>> Hello
>>
>> I have a question about the interpretation of the dissmfac function. I 
>> hope this is not stupid and understable.
>>
>> Can the pseudo-F, pseudo-R2 be interpreted and used in the same way 
>> that regression coefficients? In other words, should we pay attention 
>> to problems of collinearity? Is this really multifactorial system or 
>> simply a series of independent coefficients?
>> I ask these questions because I did an analysis with strongly 
>> correlated variables, and the results do not seem bad:
>> I would like to determine the breakpoint of an experience effect : 
>> exper = 1 to 8 and I recode it like this:
>>
>> exper1 (=1 / 2->8)
>> exper2 (= 1->2 / 3->8)
>> exper3 (= 1->3 / 4->8)
>> exper4 (= 1->4 / 5->8)
>> etc. ...
>>
>> I suspect that the experience effect is very strong at first, then 
>> gradually decreases. The results are:
>>
>> PseudoR2:
>> 7.49E-02
>> 4.57E-02
>> 3.68E-02
>> 9.54E-03
>> 7.49E-03
>> 7.44E-03
>> 4.49E-03
>>
>> The passage from one experience to two produced a change, as well as 2 
>> to 3, 3 to 4. But a break seems to occur at the 4th level of 
>> experience. Having 4 or having 5 (or 6,7 etc..) units experience seems 
>> to make not very difference (even if the 4th p-value = 0.000). Is that 
>> right?
>>
>> Thank a lot
>>
>> Alexandre
>> _______________________________________________
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>> Traminer-users at lists.r-forge.r-project.org
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>>
>>     
>
>
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