Re: st: question on zero inflated regression

[rachel grant <rachelannegrant@gmail.com>]

Sorry I meant to say the log-likelihood increases (likelihood
decreases) - I am new to all this so please bear with me! Rachel

On 17 February 2011 10:33, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> --- On Thu, 17/2/11, rachel grant wrote:
>> In my case, two of seven variables were significant
>> predictors of zeroes (both temperature), and that makes
>> sense because at low temperatures amphibians cannot move.
>
>> 1. If I change the order of the variables sometimes the p
>> value of each variable changes although the overall LR
>> and P for the model remains similar. Why does this happen
>> and what can i do about it?
>
> Sounds like your two temparature variables are highly
> colinear. In those cases there is just very little
> information in your data that can be used to distinguish
> between the effects of these two variables. You could
> take a look at -orthog- (see: -help orthog-) to transform
> these variables such that they are less correlated.
> Alternatively, you could take the position that if they
> are that correlated any one of them will contain most of
> the relevant information and you can just leave the other
> out.
>
>> 2. when I try to get a better fitting model by removing
>> nonsignificant variables the log-likelihood decreases
>> slightly. I am not sure why this happens or what to do
>
> That is exactly what should happen. The fact that a effect
> is non-significant does not mean that the effect is really
> 0. In fact, it is highly unlikely that the effect will be
> exactly 0. The fact that you included it in your model
> suggests that you thought that it could effect the outcome.
> Such variables will probably all effect the outcome, the
> question that significance tests answer is whether you
> collected enough information to detect that effect. I
> admid that this is a rather cynical interpretation of
> statistical testing, but it is not wrong. It is good to
> keep in mind that we usually test a hypothesis that we
> already know cannot be true. For your case that means that
> the likelihood should be slightly influenced when you
> leave out these variables.
>
> Typically I would leave them in my model. If I thought it
> was worth while to look at them, then I should tell my
> audience I did that and show them that the effects where
> non-significant. The easiest way to do that is to just
> leave them in my model.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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-- 
regards, Rachel

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