[R-sig-ME] Random Effect Tobit
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Wed Sep 15 03:28:43 CEST 2021
Can you explain a bit more? Is this the distinction that is
sometimes made between Gauss-Hermite quadrature and *adaptive* GHQ, i.e.
whether the expansion is centered at the population-level value or at
zero? (If so, I would *definitely* recommend GLMMadaptive, non-adaptive
GHQ can be terrible in some cases ...)
cheers
Ben Bolker
On 9/14/21 9:26 PM, Dimitris Rizopoulos wrote:
> Indeed, but the censReg does not seem to update the abscissas and
> weights during the optimization procedure.
>
> Best,
> Dimitris
>
> ——
> Dimitris Rizopoulos
> Professor of Biostatistics
> Erasmus University Medical Center
> The Netherlands
> ------------------------------------------------------------------------
> *From:* R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on
> behalf of Ben Bolker <bbolker using gmail.com>
> *Sent:* Wednesday, September 15, 2021 2:08:13 AM
> *To:* r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
> *Subject:* Re: [R-sig-ME] Random Effect Tobit
> This CrossValidated answer:
> https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstats.stackexchange.com%2Fquestions%2F544511%2Fzero-inflated-gaussian-for-weights-below-zero-recorded-as-0%3Fnoredirect%3D1%23comment999636_544511&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=YnBAIBxTozwjcNne6Mwu%2Fks6bMGD8h65RPGNk1pkiyg%3D&reserved=0
> <https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstats.stackexchange.com%2Fquestions%2F544511%2Fzero-inflated-gaussian-for-weights-below-zero-recorded-as-0%3Fnoredirect%3D1%23comment999636_544511&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=YnBAIBxTozwjcNne6Mwu%2Fks6bMGD8h65RPGNk1pkiyg%3D&reserved=0>
>
> says that the censReg package can handle random effects Tobit models
> via Gauss-Hermite Quadrature (GLMMadaptive is great, but it's nice to
> have options)
>
> On 9/14/21 3:32 AM, Dimitris Rizopoulos wrote:
>> You can use the GLMMadaptive package (https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdrizopoulos.github.io%2FGLMMadaptive%2F&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=fPFues1lFb26w8DBx2Pg2RvbCRZT3Uq7Pd3QQUbfeic%3D&reserved=0
> <https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdrizopoulos.github.io%2FGLMMadaptive%2F&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=fPFues1lFb26w8DBx2Pg2RvbCRZT3Uq7Pd3QQUbfeic%3D&reserved=0>)
> and the censored.normal() family object. An example is given here:
> https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdrizopoulos.github.io%2FJMbayes2%2Farticles%2FNon_Gaussian_Mixed_Models.html%23censored-linear-mixed-models-1&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=tKMo%2BIYYy3M8mdUf5KdLjvk%2BCSC0QxGvfLRk6Vln3rU%3D&reserved=0
> <https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdrizopoulos.github.io%2FJMbayes2%2Farticles%2FNon_Gaussian_Mixed_Models.html%23censored-linear-mixed-models-1&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C40b92da1072c42854dfd08d977dcf8ed%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637672613225156938%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=tKMo%2BIYYy3M8mdUf5KdLjvk%2BCSC0QxGvfLRk6Vln3rU%3D&reserved=0>
>>
>>
>> Best,
>> Dimitris
>>
>> -----Original Message-----
>> From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Hedyeh Ahmadi
>> Sent: Tuesday, September 14, 2021 12:25 AM
>> To: r-sig-mixed-models using r-project.org
>> Subject: [R-sig-ME] Random Effect Tobit
>>
>> Hello all,
>> I was wonder if anyone is aware of an R package with random effect Tobit. Any help would be greatly appreciated.
>>
>> Best,
>>
>> Hedyeh Ahmadi, Ph.D.
>> Statistician
>> Keck School of Medicine
>> Department of Preventive Medicine
>> University of Southern California
>>
>> Postdoctoral Scholar
>> Institute for Interdisciplinary Salivary Bioscience Research (IISBR) University of California, Irvine
>>
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> --
> Dr. Benjamin Bolker
> Professor, Mathematics & Statistics and Biology, McMaster University
> Director, School of Computational Science and Engineering
> Graduate chair, Mathematics & Statistics
>
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Director, School of Computational Science and Engineering
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