[R list package] Computational Problems Using 'List' Package

[David Szakonyi]

Yes, it does pass the design test (p-value of 0.9384607). I'm going to try
the Bayes model. Any other thoughts about why this would be happening?

Thanks,

David


On Wed, Feb 26, 2014 at 9:41 AM, Kosuke Imai <kimai at princeton.edu> wrote:

> Does your data pass the design test we've developed?  Sometimes, the
> skewed data leads to this type of problems.  Another possibility is to use
> a Bayesian model, which is available through ictregBayes().
>
> Kosuke Imai
> Department of Politics
> Princeton University
> http://imai.princeton.edu
>
>
> On Tue, Feb 25, 2014 at 6:44 PM, David Szakonyi <ds2875 at columbia.edu>wrote:
>
>> Hello!
>>
>> I've been analyzing a two treatment list experiment that has been
>> properly randomly assigned.
>>
>> I just ran the following regression with one simple explanatory bivariate
>> variable (gender), but received the following error:
>>
>> > ml.resultsnp <- ictreg(outcome ~ male, data = true_subsetnp, treat =
>> "treatstatnp", J=4, method = "ml")
>>
>> Error in solve.default(-MLEfit$hessian) :
>>   system is computationally singular: reciprocal condition number =
>> 7.02376e-18
>>
>> I then added an additional covariate (logged age), and the model
>> converges, but with exploding standard errors:
>>
>> Item Count Technique Regression
>>
>> Call: ictreg(formula = outcome ~ male + lage, data = true_subsetnp,
>>     treat = "treatstatnp", J = 4, method = "ml")
>>
>> Sensitive item (1)
>>                 Est.    S.E.
>> (Intercept)  3.94811 4.15077
>> male        -1.43966 1.21671
>> lage        -1.78966 1.14337
>>
>> Sensitive item (2)
>>                  Est.       S.E.
>> (Intercept)  -7.93707   10.31522
>> *male        -16.06552 2965.82521*
>> lage          0.99201    2.63957
>>
>> Control items
>>                 Est.    S.E.
>> (Intercept)  0.21267 0.25312
>> male         0.00017 0.05277
>> lage        -0.04225 0.06603
>>
>> Log-likelihood: -2039.366
>>
>> Number of control items J set to 4. Treatment groups were indicated by
>> '1' and '2' and the control group by '0'.
>>
>> A variety of other model specifications return very similar results:
>> either failing to converge/compute or returning point estimates and very
>> large standard errors (sometimes nearly exactly the same values for not at
>> all correlated variables).
>>
>> Does anyone have any suggestions about what might be going wrong?
>>
>> Thanks,
>>
>> David
>>
>>
>> --
>> David Szakonyi
>> Ph.D Candidate - Comparative Politics
>> Columbia University
>> ds2875 at columbia.edu
>>
>> _______________________________________________
>> listpackage-discuss mailing list
>> listpackage-discuss at lists.r-forge.r-project.org
>>
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/listpackage-discuss
>>
>
>


-- 
David Szakonyi
Ph.D Candidate - Comparative Politics
Columbia University
ds2875 at columbia.edu
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