test for colinearity

[#1]

I have a nominal outcome variable (ICU admission yes/no)

Predictor variables are both categorical (like presence of some symptoms yes/no) as well as continuous (like age body mass index)

There is however a potential for co-linearity between these predictor variables.

The nominal logistic regression model does not give me a option to calculate VIF upon right clicking the parameter estimates.

What would be the best way to assess collinearity in such instance?

thank you

Sandeep

[#2]

Consider changing to a generalized linear model with a binomial distribution and logit link function. You can then see the correlation of estimates. It is not a VIF, but it will give you some information on the collinearity.

Otherwise, you will need to explore the relationships among the predictors outside of the Fit Model platform. 

[#3]

Thanks Dan. I was able to obtain the correlation of estimates.

Can you provide some guidance on how to interpret these..should I interpret them as correlation coefficients ?

[#4]

As a start, use the Analyze>Multivariate Methods>Multivariate platform.  Put all of the variables in the Y, Columns window.  You'll get correlation coefficients and more importantly a scatter plot matrix to look for collinear relationships.

[#5]

thanks..this platform however restricts to numeric variables.

I have a combination of continuous and categorical variables

[#6]

You can actually still do it by changing all of the variables to continuous just for this platform. I'm not interested in the statistics per se, but the plots. Agreed the categorical variables will look a bit strange, but obvious relationships can show up. For example, in the image I have attached I have categorical factors of Shift and Technician with an ordinal Y.  You can see, obviously, the correlation of shift and tech.