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I am running a logistic regression on financial data of several companies. The dependent variable is the company to be a smoother (binary variable: 0, 1), and there are several independent variables: company age, company size, number of board meetings, etc. I tried to run a glm that detect any complete separation in this model through the R-package "detectseparation." The code in R was:

detect<-glm(formula=smth~0+Big4+age+lev+assets_log+committee+PctIndependentDirectors+meetings+boardsize+afe+value_log+revgrowth_log,data=data2,family = binomial("logit"),method = "detect_separation")

the result was: enter image description here

We can see that there is always 0 next to each IV. What does 0 mean? How about -inf and +inf?

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As it says in the printout, these values indicate existence of the maximum likelihood estimates. 0 means they are finite (i.e. no complete separation), whereas infinite values are for completely separated predictors (0 for -Inf and 1 for Inf respectively).

You should use the standard glm methods like summary and anova to extract parameter estimates and inferences - all this object's print shows you right now is that there don't seem to be any complete separation issues.

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