Dear Carlos,
On 2024-06-19 11:13 a.m., Carlos Barboza wrote:
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Thank you Jonh for you answer
I indeed used contr.sum, contr.poly before to model definition
after that I get the same results using any names for the same factors
but, what do you exactly mean when testing for factors for differences
over the origin?
The context here is models with both numeric and factor predictors.
Consider a model of the form y ~ x*f, where x is numeric and f is a
factor. A type-III test for the "main effect" of f tests for differences
among factor levels where x = 0 (the origin).
More generally, the issues concerning "types" of tests in models with
linear predictors are sufficiently complicated that discussing them in a
help file or by email is likely to prove unsatisfactory. See the first
two references in ?Anova for more details.
I hope this helps,
John
best regards
Carlos
Em qua., 19 de jun. de 2024 ?s 10:14, John Fox <jfox at mcmaster.ca
<mailto:jfox at mcmaster.ca>> escreveu:
Dear Carlos,
From ?Anova:
"Warning
Be careful of type-III tests: For a traditional multifactor ANOVA
with interactions, for example, these tests will normally only be
sensible when using contrasts that, for different terms, are
in the row-basis of the model, such as those produced by contr.sum,
contr.poly, or contr.helmert, but not by the default
contr.treatment. In
a model that contains factors, numeric covariates, and interactions,
main-effect tests for factors will be for differences over the
In contrast (pun intended), type-II tests are invariant with respect
(full-rank) contrast coding. If you don't understand this issue, then
you probably shouldn't use Anova for type-III tests."
I hope this helps,
John
--
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://www.john-fox.ca/ <https://www.john-fox.ca/>
--
On 2024-06-18 5:59 a.m., Carlos Barboza wrote:
> [You don't often get email from carlosambarboza at gmail.com
>
> Caution: External email.
>
>
> sorry, Anova type III function form car package in R
>
> Em ter., 18 de jun. de 2024 ?s 06:59, Carlos Barboza <
> carlosambarboza at gmail.com <mailto:carlosambarboza at gmail.com>>
>> Dear all,
>> why Anova type III function gives different results if I change
>> of a categorical factor? I suspect that is because contrast type
>> something strange get different results using the same data.
>> thank you
>>
>> Em s?b., 7 de mai. de 2016 ?s 12:26, Carlos Barboza <
>> carlosambarboza at gmail.com <mailto:carlosambarboza at gmail.com>>
>>> Dear Dr. Ben Bolker
>>>
>>> My name is Carlos Barboza and I am a Marine Biologist from the
>>> Janeiro University, Brazil. First it's a pleasure to again have
>>> opportunity to send you a message.The reason for it is a simple
>>> Can I compare AIC from:
>>>
>>> 1. glmmADMB: Density ~ 1 + 1|Site
>>>
>>> 2. glmmADMB: Density ~ Sector + 1|Site + Cage
>>>
>>> Note that they have different random and fixed structures. I
>>> this is not the best choice to model selection but, I think
>>> values can be compared.
>>>
>>> thank you very much for your attention
>>>
>>>
>>> is Cage a random effect? Are you intentionally leaving out
>>> intercept in the second case (it will be included anyway unless
>>> use -1)? In any case, I don't see any obvious reason you can't
>>> compare AIC values; see
>>>
>>>
>>>
>>> Follow-ups to r-sig-mixed-models at r-project.org
<mailto:r-sig-mixed-models at r-project.org>, please ...
>>>
>>> sorry, yes, cage was included only to examplify a different
>>> structure in the second case...it should be coded (1|Site) +
>>> yes, I know that the intercept will be included in the second
>>>
>>> it's an example of comparing AIC values from mixed models with
>>> fixed and random structures:
>>>
>>> 1. Density ~ 1 + 1|Site
>>>
>>> 2. Density ~ Sector + 1|Site + 1|Cage
>>>
>>> comparing AIC...I beleive that both values can be compared
>>>
>>> again, thank you very much for your very fast message
>>>
>>>
>>>
>>>
>>
>> --
>> Universidade Federal do Rio de Janeiro (UFRJ)
>> Instituto de Biodiversidade e Sustentabilidade - NUPEM
>> Caixa Postal 119331, CEP 27910-970
>> Maca?, RJ, Brazil
>> https://www.macae.ufrj.br/nupem/ <
>
>
> --
> Universidade Federal do Rio de Janeiro (UFRJ)
> Instituto de Biodiversidade e Sustentabilidade - NUPEM
> Caixa Postal 119331, CEP 27910-970
> Maca?, RJ, Brazil
> https://www.macae.ufrj.br/nupem/ <
>
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