Hi Suzan,
You can do sort of backtransformation inside of ggplot2
(http://had.co.nz/ggplot2).
library(ggplot2)
# Create the base scatterplot with y and x axes transformed by logging,
# and then back transformed by exponentiating
(base <- qplot(carat, price, data=diamonds) + scale_x_log10() +
sca
My question is related to plot( ) in the mgcv package. Before modelling
the data, a few predictors were transformed to normalize them.
Therefore, the x-axes in the plots show transformed predictor values.
How do I back-transform the predictors so that the plots are easier to
interpret?
Than
