How to run logistic regression with state variables?

I am trying to run a logistic regression analysis of default/non-default for coporate bonds,

prob of default = 1/(1+exp(-f(x)), f(x) = b0 + b1*x1 + b2*x2

x1 (Dist_to_DFLT) is a continouse variable, and x2 (CreditStateCategory) is a state variable with 4 different states. I am following the example here to fit a Logistic Regression model.

https://www.mathworks.com/help/stats/fitglm.html?s_tid=doc_ta

My code is:

modelspec1 = 'DefaultFlag ~ Dist_to_DFLT*CreditStateCategory - Dist_to_DFLT:CreditStateCategory';

mdl1 = fitglm(Analytics_Data_AllPeriods_Train,modelspec1,'Distribution','binomial')

And the output is:

mdl1 =

Generalized linear regression model:

logit(DefaultFlag) ~ 1 + Dist_to_DFLT + CreditStateCategory

Distribution = Binomial

Estimated Coefficients:

Estimate SE tStat pValue

________ ________ _______ __________

(Intercept) -1.5476 0.031128 -49.716 0

Dist_to_DFLT -1.2886 0.017317 -74.411 0

CreditStateCategory_Expansion 0.074652 0.045428 1.6433 0.10032

CreditStateCategory_Recovery -0.86881 0.058402 -14.876 4.703e-50

CreditStateCategory_Repair -0.97184 0.084661 -11.479 1.6794e-30

169282 observations, 169277 error degrees of freedom

Dispersion: 1

Chi^2-statistic vs. constant model: 8e+03, p-value = 0

My question is why my regression model doesn't generate the cross-terms like: Sex*Age, Sex*Weight, Age*Weight etc?

mdl =

Generalized linear regression model:

logit(Smoker) ~ 1 + Sex*Age + Sex*Weight + Age*Weight

Distribution = Binomial

Answer 1

[the cyclist]

When you specified the model as

modelspec1 = 'DefaultFlag ~ Dist_to_DFLT*CreditStateCategory - Dist_to_DFLT:CreditStateCategory'

the "subtracted" term

'- Dist_to_DFLT:CreditStateCategory'

is explicitly removing the cross term.

It seems that you actually intended

modelspec1 = 'DefaultFlag ~ Dist_to_DFLT*CreditStateCategory'