I am trying to perform a logistic regression with the lm() function in R. My model is: lm(xrd ~ VariableA*Post, data = DatasetXRD), this is a difference-in-differences model, the R code is based on: https://www.princeton.edu/~otorres/DID101R.pdf.
Some general info regarding my data:
I have applied pseudo adoption in my model (in the Post variable). So I state that some companies will apply a certain rule after a year even though they do not apply it. However VariableA will remain 0 (no application of the rule) for these companies. This will result into a value of 1 for companies that do apply it, and a value of 0 for companies that do not apply it (in that specific year, it could be that they will apply it in a later year).
VariableA and Post are both dummy variables (value= 0 or 1).
The third row of text in my lm table is showing NAs.
Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 43286 4865 8.897 <2e-16 *** VariableA 4900 6362 0.770 0.441 Post -4904 6849 -0.716 0.474 VariableA:Post NA NA NA NA
As shown in the table this is because of singularities. After a google search I have found that this is because of collinearity.
I have run the cor() function trying to see if this would lead to a perfect correlation, since that would proof that I have collinearity issues, but I don't find the results convincing (am I making a thinking error here?)
This leads to the following output: 0.5890362. Correlation is not 1, so that does not mean that my independent variables are not perfectly collinear (in my own words: they do not perfectly explain each other?)
I have also ran:
alias(didreg, complete = TRUE, partial = FALSE, partial.pattern = FALSE)
I have read in a previous question that was similar that this will show collinearity, however I will admit that I do not fully understand how to interpret the output of the table below.
Model : xrd ~ VariableA * Post Complete : (Intercept) VariableA Post VariableA:Post 0 1 0
I do not understand why I am having collinearity problems. My Variable A and Post variable are correlated, but only for 0.58, not for 1...
If I have missed some important pieces, please let me know.
Thanks in advance for any help!