# invResPlot

##### Inverse Response Plots to Transform the Response

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`

.

- Keywords
- regression, hplot

##### Usage

`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, ...)

##### Arguments

- model
A

`lm`

regression object- lambda
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

- xlab
The horizontal axis label. If

`NULL`

, it is constructed by the function.- labels
Case labels if labeling is turned on; see

`invTranPlot`

and`showLabels`

for arguments.- robust
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`

.

##### Value

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.

##### References

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.

##### See Also

##### Examples

```
# NOT RUN {
m2 <- lm(rate ~ log(len) + log(adt) + slim + shld + log(sigs1), Highway1)
invResPlot(m2)
# }
```

*Documentation reproduced from package car, version 2.1-6, License: GPL (>= 2)*