For a lm model, draws an inverse.response plot with the response \(Y\) on the
vertical axis and the fitted values \(\hat{Y}\)
on the horizontal axis. Uses nls to
estimate \(\lambda\) in the function
\(\hat{Y}=b_0+b_1Y^{\lambda}\).
Adds the fitted curve to the plot.
invResPlot is an alias for inverseResponsePlot.
inverseResponsePlot(model, lambda=c(-1,0,1), robust=FALSE, xlab=NULL, ...)# S3 method for lm
inverseResponsePlot(model, lambda=c(-1,0,1), robust=FALSE,
xlab=NULL, labels=names(residuals(model)), ...)
invResPlot(model, ...)
A lm regression object
A vector of values for lambda. A plot will be produced with curves corresponding to these lambdas and to the least squares estimate of lambda
The horizontal axis label. If NULL, it is constructed by the function.
Case labels if labeling is turned on; see
invTranPlot and showLabels for arguments.
If TRUE, then estimation uses Huber M-estimates with the median absolute deviation to estimate scale and k= 1.345. The default is FALSE.
Other arguments passed to invTranPlot and then to
plot.
As a side effect, a plot is produced with the response on the horizontal axis and fitted values on the vertical axis. Several lines are added to be plot as the ols estimates of the regression of \(\hat{Y}\) on \(Y^{\lambda}\), interpreting \(\lambda\) = 0 to be natural logarithms.
Numeric output is a list with elements
Estimate of transformation parameter for the response
The residual sum of squares at the minimum if robust=FALSE. If robust = TRUE, the value of Huber objective function is returned.
Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.
Pendergast, L, and Sheather, S. (in press). On sensitivity of response plot estimation of a robust estimation approach. Scandinavian Journal of Statistics.
Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley, Chapter 7.
# NOT RUN {
m2 <- lm(rate ~ log(len) + log(adt) + slim + shld + log(sigs1), Highway1)
invResPlot(m2)
# }
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