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Correlation between covariates and intercept (spatstat)

2 messages · Adrian Baddeley, Virginia Morera Pujol

Adrian Baddeley
# 
Virginia Morera Pujol <morera.virginia at gmail.com> writes:
This is about the correlation between *estimates* of the model coefficients - in this case, the correlation between the estimated intercept and the estimated coefficient of the distance covariate. Extremely high correlations could cause problems with the identifiability of the model, but this is probably not a problem here. Moderately high correlations suggest that the t-tests for individual parameters (given in the printout for the model) are not independent. If we want to select the 'significant' covariates, we shouldn't use the model printout to discard more than one variable at a time.
Such transformations will change the correlation. Roughly speaking, that's because when you add a constant to the distance covariate, you are adding a multiple of the intercept onto the covariate. 

When you say the 'effect' of the covariate has increased, do you mean the coefficient of the covariate has increased, or the *effect term* (= coefficient x covariate value) has increased? I'd be surprised if this happens - the models should be equivalent as regards their fitted intensity, etc.

Adrian Baddeley


Prof Adrian Baddeley DSc FAA
Department of Mathematics and Statistics
Curtin University, Perth, Western Australia
Virginia Morera Pujol
# 
Hello,

You are right, what changes is the coefficient of the covariate, but it's
effect (coef+covariate value) is the same in both models, and selecting the
"significant covariates" won't be much of a problem as I only have 2
covariates in my model.

Thank you very much for your kind help!

Virginia Morera
PhD Student
Department of Animal Biology
University of Barcelona

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2016-04-17 12:36 GMT+02:00 Adrian Baddeley <adrian.baddeley at curtin.edu.au>: