Hi All,
I wonder if you can help me with an aparently simple task. I have been
searching examples for this without any luck:
#Assume
x<-1:10 #x ranges from 1 to 10.
y<-x*runif(10)+ 1.5*x #y is a linear function of x with some error. Add
uniform error that is scaled to be larger as x values also become larger
#error is proportional to x size, this should cause heterocedasticity.
#I know there are many methods to deal with heterocedasticity, but in my
specific case, I want to use percent regression to minimize the mean absolute
#percentual error as opposed to regular regression that deals with the square
of the errors.
#Question, how to fit a linear model to minimize this error on the data y ~ x
above?
#Please do not use model<-lm(y ~ x....) as this will minimize the square of the
errors, not the mean absolute percent error
Best regards, André Cesta
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