I am trying to perform a robust regressions using the lmrob function in R. I am getting this error Message:
In lmrob.S(x, y, control = control) :
S-estimated scale == 0: Probably exact fit; check your data
Then I see that 244 of my observations are weighted with 0, the other 355 are weighted with 1 (see plot as illustration). My criterion variable is a continuous variable ranged 1-5, but with a lot of mass at 1.
In fact, any other value than the 1 was considered as an outlier in the
Regardless of whether it is appropriate to finally rely on a linear regression model given these circumstances, is there an intuitive way to adjust the "threshold" to consider an observation as outlier when using this function? I have never worked with such a model or this function before, but it seems like a potential solution to me to increase the threshold in a way that prevents the model from considering half of the sample as outliers. However, the explanations in the help sections are too technical for me.
Or is it more appropriate to exclude outliers before fitting the model? The reason I fitted the robust model was to compare it to the results of a normal OLS regression and a bootstrap regression.