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I am using the RMS package in R to conduct a logistic regression that contains a three-way interaction. As part of my modelling approach, I have conducted chunk tests of the interaction (using Wald chi-square) followed by odds ratios for significant interactions. I have been asked by a reviewer to provide betas and standard errors for all factors in the model. I know how to obtain beta estimates for different levels of an interaction, but I can't find a way to obtain a single beta to represent the entire three-way interaction term. Is there any way of doing this with an lrm object?

EDIT:

Model is predicting police outcome (court/no court) Independent variables are ethnicity (binary), age (continuous), sex (binary), previous violent charge (3 categories) and number of charges (3 categories).

Model

model<-fit.mult.impute(Outcome~ rcs(Age,5) + Ethnicity + Violence + Sex + N_Charges + rcs(Age,5)*Ethnicity + rcs(Age,5)Sex + EthnicityN_Charges+ Ethnicity*Violence + rcs(Age,5)*Ethnicity*Sex, x=TRUE, y=TRUE, fitter = lrm, xtrans = imp)

I am interested in obtaining the beta for the bolded interaction term.

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  • $\begingroup$ Could you include said model and a description of what each variable is? $\endgroup$ – Frans Rodenburg Aug 27 '18 at 3:36
  • $\begingroup$ I have made an edit to include this information. I am not sure if that helps. $\endgroup$ – user183974 Aug 27 '18 at 3:44

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