I'm having trouble interpreting the results from the Spread-Level Plot function in R (car package). The documentation says:
spread-stabilizing power transformation, calculated as 1 - slope of the line fit to the plot.
This is not explicit enough for me. Should this transformation be applied to every variable in the regression?
For example, assume I have an lm object given by:
myFit <- lm(y ~ x1 + x2)
Then I use Spread-Level Plot:
If the 'suggested power transformation' is 0.5, then does that imply a homoscedastic model could be fit using one of the following?
refitA <- lm(sqrt(y) ~ sqrt(x1) + sqrt(x2)) refitB <- lm(sqrt(y) ~ x1 + x2) refitC <- lm(sqrt(y) ~ sqrt(x1 + x2))
If I understand correct, refitA would be the suggested model to approximate homoscedasticity. On the other hand, if I only want to transform the LHS, I would use the
powerTransform function (also from car package). i.e., an "estimated transform parameter" of 0.5 from the powerTransform function would imply that refitB is homoscedastic.
Is this correct?