I get a fan-shaped scatter plot of the relation between two different quantitative variables:
I am trying to fit a linear model for this relation. I think I should apply some kind of transformation to the variables in order to unify the ascent variance in the relation before fitting a linear regression model, but I can't find the way to do it. Or maybe, there is a better model to use in these cases, I can't either find it.
I have tried rlm
, but the residuals still have heteroscedasticity. I have also tried to apply a SD ratio calculated from all the y of each x and other similar erratic approaches.
My questions:
- Is there any typical way of fitting a model for a fan-shaped relation or a typical model to use in these cases?
- Is there any typical transformation that could be applied to the variables in order to reduce its variance?
gls
in package nlme allows specifying a heteroscedasticity structure. $\endgroup$lm
, but I don't know how to take advantage of them. I'll trygls
, too, thanks @Roland. The relation is weaker for higher values of the predictor, but I don't know how to figure out the heteroscedasticity structure in order to apply it to theweights
or pre-transform the data. I am really lost with this. $\endgroup$