[R] How to fit a linear model to data by minimizing the mean absolute percent error?

[Andre Cesta]

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