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Jun
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comment When forcing intercept of 0 in linear regression is acceptable/advisable
You both just play with words. The usual understanding of 0 + or -1 in lm is removing global intercept, which it actually does. BTW, I say exactly the same as you both do in the last sentence of my answer, so I don't really get why someone downvoted.
Jun
10
answered When forcing intercept of 0 in linear regression is acceptable/advisable
May
31
comment Prerequisites for AIC model comparison
I don't understand what you follow with your attempt to "correct" AIC somehow and what did you actually get by it (how to interpret your result). Anyway, don't dig into this, it doesn't matter because my question was about something completely different: what are the general prerequisites for the AIC (actual, uncorrected) to be sensibly comparable. Don't focus on this particular example, it's just an example of the general thing.
May
30
awarded  Popular Question
May
26
comment When is it ok to remove the intercept in lm()?
You missed the point of Joshua Example 1 and seem to still ignore it completely. In models with categorical covariate the removal of the intercept results in the same model with just different parametrization. This is a legitimate case when intercept can be removed.
May
8
awarded  Taxonomist
Apr
27
comment JAGS: unobserved parents and intialization
stats.stackexchange.com/q/17121/5509
Apr
15
awarded  Popular Question
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@fabians yes, for some species I have a lots of very small $N_{i,j}$ - like 0, 1, 2, 3, i.e. small numbers per site (low density), yet they have a very reliable data because they are present at a large number of sites. Anyway, I get your point with gradually building complexity, but how do I know if my simpler model is good enough? Comparing with the result of "perfect" bayesian approach with the simple model on several species?
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@fabians note aside, the model I proposed is already used in the literature, look at the citation here. Unfortunatelly the transformation described there only works for simpler model without the random effect $\gamma_{j}$.
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@fabians I get your point about the simplicity. However I don't agree on points 1, 2 - I insist my approach goes along the GLM line (see my equation in pt. 2 above) and it doesn't make sense to bring $N$ instead of $\mu$ to the equation, be it right or left side. Well, but if you say "OK, your approach is theoretically correct but don't be so strict and just go for simple solution for practical reasons", I get your point! Maybe I complicate things to much, but I simply hate "solutions" like $log(x+1)$. Why 1? Why not 0.5 or 0.01 or 0.0001? I think there's a lot of dirt in the simple solution.
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@fabians and 2) if you rewrite the model as $log(\mu_{i,j+1}/\mu_{i,j}) = \alpha + \beta x_{i,j} + \gamma_j$, then it looks like you are modelling the population growth and it perhaps will make much more sense to you than if you wrote $log(\mu_{i,j+1}/N_{i,j}) = ...$. And finally 3) How would you do $log(N_{i,j})$ when $N_{i,j}$ is zero? This is the way GLM models work.
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@fabians - I was thinking on this line too but I came to conclusion that this is not correct way to do it, for several reasons: 1) you wrote: "what affecs the subsequent time period is not the theoretical/latent quantity \mu but the size of the pop. that actually was present previously" - well, you could say the same about the $\mu_{i,j+1}$ on the left side too - but this is how the model values $\mu$ work, they are theoretical model coefficients bound by the population model this way and I think it's correct. This is how GLM models work in general.
Mar
28
comment How to fit log-linear poisson autoregressive mixed model?
@probabilityislogic I can write the model in JAGS or WinBUGS, but it takes too long to compute. BayesX does the same? How can something called BayesX be based on REML, I thought it is frequentist methond. PS: $\mu_{i,0}$ can be treated as a model parameter if necessary (i.e. it would get an uninformative prior in bugs).
Mar
26
revised How to fit log-linear poisson autoregressive mixed model?
added 3 characters in body; edited title