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From |
rachel grant <rachelannegrant@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: question on zero inflated regression |

Date |
Thu, 17 Feb 2011 11:55:24 +0000 |

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 > -------------------------- > > > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- regards, Rachel * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: question on zero inflated regression***From:*Nick Cox <n.j.cox@durham.ac.uk>

**References**:**Re: st: question on zero inflated regression***From:*rachel grant <rachelannegrant@gmail.com>

**Re: st: question on zero inflated regression***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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