Negative variables composite indicator

Good morning,
I have a problem with my dataset on R. I would like to perform some composite indicators by assigning a weight to each variable. Unfortunately, I have to deal with some variables that have a negative connotation (e.g. number of closed firms on competitiveness), but I do not know how to manage this problem.
The problem is that if I assign weights, in this way I say that the larger the number of closed enterprises in a region, the higher the competitiveness. I would like to find a way to assign a variable as negative.
Thank you very much for your time


Welcome to the RStudio community!

If you like to create this whole expression manually you could simply divide the weights by the variables that have negative impact.

Competetiveness = W1 x Pos1 + W2 x Pos2 + W3 / Neg1 + ... 

Another way is to make the weights negative for these variables

Competetiveness = W1 x Pos1 + W2 x Pos2 - W3 x Neg1 + ... 

In both cases, the larger the negative variables, the lower the competitiveness. Note that the first expression can only be positive, whilst the second one could be negative depending on the values of the weights and indicators.

If you were doing the opposite and trying to learn the weights (instead of setting them yourself), you'd be doing a linear regression, which has the same type of formula.

Hope this helps,

Thank you very much for your kind reply and your very polite welcome. I understood the idea perfectly. But how can I do this if I have to use indicators such as Fisher, Liptak or Tippett?
Thank you again and I apologise for the additional question.


I'm afraid I don't understand what you mean by that. I think these indicators your are talking about are functions, but I don't understand how you like to use them.

Please provide a more detailed example, preferably with some code and data by creating a reprex. A reprex consists of the minimal code and data needed to recreate the issue/question you're having. You can find instructions how to build and share one here:


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