Is there a way to calculate the regression coefficients for individuals instead of just a group resp. calculating regression coefficients for a very small sample size?
My goal is to test different ways to cluster/segment a group of people. In the domain in question the best variables to use for segmentation would be variables that best express the importance an individual places on certain attributes.
However I do not have direct measurement of that importance. What I do have is an indirect measurement for the whole sample based on a shapley value regression of attribute ratings and final respondent choice.
Clearly put, I asked a couple of people how they would rate the attributes of a given brand and which brand they would choose in the end.
Now I wonder whether it is possible to obtain this indirect importance measurement also on an individual level which would mean either:
- Extracting individual coefficient estimators from the overall regression model ( I don't believe that is possible)
- Calculating a regression for each individual (Don't know how to do that hence the question)
Problem and what I tried
I work in
R and I do know how to fit a grouped linear model. As all my respondents rated two brands I do have n = 2 per group.
However fitting a normal grouped linear model results in unusable results where almost all coefficients are NA and one or two equal 1. Additionally I would prefer to fit a relative importance or shapley value regression but the
relaimpo package throws an error complaining about too few observations.
What other avenues could I pursue?
I have been using this code so far:
df %>% group_by(i_TAN) %>% do(model = lm(formula = Consideration ~ ., data = .))