0
$\begingroup$

I'd like to use SPSS Generalized Linear Model to analyze a dataset of insects collected from one particular species of vegetables.

I have following variables:

NUMBER (number of insects collected)

SITE COVERED (true/false) - some of the sites were covered with plastic in order to create dry and wet conditions there

STAGE OF DEVELOPMENT (1,2,3) - stage of development of a vegetable in particular site (fresh, mature, old)

SITE NUMBER (1-10)

80% of data points are zeros

I want to check if SITE COVERED and STAGE OF DEVELOPMENT affect NUMBER, but also make sure that SITE NUMBER has no influence on NUMBER.

My model is SITE COVERED + STAGE OF DEVELOPMENT + STAGE OF DEVELOPMENT*SITE COVERED

But how am I supposed to include SITE NUMBER (as a random effect) in order to get a full picture of interactions? As far as I know, zero-inflated Poisson model should work out here. But how to include a random factor in SPSS?

Please correct me if I'm wrong in my suggestions.

P.S. also I'm considering use of R, since it has some packages for zero-inflated models, however, I didn't find allowing easily to include a random factor

$\endgroup$
1
$\begingroup$

What you're asking for is sometimes called a generalized linear mixed model (GLMM). I don't know about SPSS, but in R you can estimate them with any of the nlme, lme4, or MCMCglmm packages. Ben Bolker has written a very nice demonstration of using MCMCglmm to fit a zero-inflated Poisson model, available here.

$\endgroup$
1
  • $\begingroup$ great advice, thanks a lot! I managed to train the model and learn about different ways of doing it. It was an incredible starting point which I exactly needed. Unfortunately, I can't add deserved points to your reputation, but probably someone else may read this comment and press "up" :) $\endgroup$
    – Igor Andri
    Mar 25 '15 at 12:53

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.