invTranPlot
draws a two-dimensional scatterplot of $Y$ versus 
$X$, along with the OLS
fit from the regression of $Y$ on 
$(X^{\lambda}-1)/\lambda$.  invTranEstimate
finds the nonlinear least squares estimate of $\lambda$ and its
standard error.invTranPlot(x, ...)
## S3 method for class 'formula':
invTranPlot(x, data, subset, na.action, ...)
## S3 method for class 'default':
invTranPlot(x, y, lambda=c(-1, 0, 1), robust=FALSE,
        lty.lines=rep(c("solid", "dashed", "dotdash", "longdash", "twodash"), 
        length=1 + length(lambda)), lwd.lines=2, 
        col=palette()[1], col.lines=palette(),
        xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
        family="bcPower", optimal=TRUE, key="auto",
        id.method = "x",
        labels, 
        id.n = if(id.method[1]=="identify") Inf else 0,
        id.cex=1, id.col=palette()[1], grid=TRUE, ...)
invTranEstimate(x, y, family="bcPower", confidence=0.95, robust=FALSE)lm, select a subset of the caseslm, the action for missing dataFALSE."bcPower", 
  "yjPower", or a user-defined family.FALSE, or if robust=TRUE,
no interval is returned.pch argument, rather than colors.palette"auto", in which case a legend is added to
the plot, either above the top marign or in the bottom right or top right corner.
Set to NULL to suppress the legend.id.n=0 for labeling no points.  See
    showLabels for details of these arguments.pch.invTranPlot
  plots a graph and returns a data frame with $\lambda$ in the 
  first column, and the residual sum of squares from the regression
  for that $\lambda$ in the second column.
  invTranEstimate returns a list with elements lambda for the
  estimate, se for its standard error, and RSS, the minimum
  value of the residual sum of squares.inverseResponsePlot,optimizewith(UN, invTranPlot(gdp, infant.mortality))
with(UN, invTranEstimate(gdp, infant.mortality))Run the code above in your browser using DataLab