# R: Choose a Predictor Transformation Visually or Numerically

invTranPlot {car} | R Documentation

## Choose a Predictor Transformation Visually or Numerically

### Description

`invTranPlot` draws a two-dimensional scatterplot of Y versus X, along with the OLS fit from the regression of Y on (Y^(lam)-1)/lam. `invTranEstimate` finds the nonlinear least squares estimate of lambda and its standard error.

### Usage

    invTranPlot(x, ...)

    ## S3 method for class 'formula'
    invTranPlot(x, data, subset, na.action, id=FALSE, ...)

    ## Default S3 method:
    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=carPalette()[1], col.lines=carPalette(),
            xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
            family="bcPower", optimal=TRUE, key="auto", id=FALSE,
            grid=TRUE, ...)

    invTranEstimate(x, y, family="bcPower", confidence=0.95, robust=FALSE)

### Arguments

`x` | The predictor variable, or a formula with a single response and a single predictor
`y` | The response variable
`data` | An optional data frame to get the data for the formula
`subset` | Optional, as in `lm`, select a subset of the cases
`na.action` | Optional, as in `lm`, the action for missing data
`lambda` | The powers used in the plot. The optimal power than minimizes the residual sum of squares is always added unless optimal is `FALSE`.
`robust` | If `TRUE`, then the estimated transformation is computed using Huber M-estimation with the MAD used to estimate scale and k=1.345. The default is `FALSE`.
`family` | The transformation family to use, `"bcPower"`, `"yjPower"`, or a user-defined family.
`confidence` | returns a profile likelihood confidence interval for the optimal transformation with this confidence level. If `FALSE`, or if `robust=TRUE`, no interval is returned.
`optimal` | Include the optimal value of lambda?
`lty.lines` | line types corresponding to the powers
`lwd.lines` | the width of the plotted lines, defaults to 2 times the standard
`col` | color(s) of the points in the plot. If you wish to distinguish points according to the levels of a factor, we recommend using symbols, specified with the `pch` argument, rather than colors.
`col.lines` | color of the fitted lines corresponding to the powers. The default is to use the colors returned by `carPalette`
`key` | The default is `"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.
`xlab` | Label for the horizontal axis.
`ylab` | Label for the vertical axis.
`id` | controls point identification; if `FALSE` (the default), no points are identified; can be a list of named arguments to the `showLabels` function; `TRUE` is equivalent to `list(method=list(method="x", n=2, cex=1, col=carPalette()[1], location="lr")`, which identifies the 2 points with the most extreme horizontal values — i.e., the response variable in the model.
`...` | Additional arguments passed to the plot method, such as `pch`.
`grid` | If TRUE, the default, a light-gray background grid is put on the graph

### Value

`invTranPlot` plots a graph and returns a data frame with lam in the first column, and the residual sum of squares from the regression for that lam 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.

### Author(s)

Sanford Weisberg, sandy@umn.edu

### References

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Prendergast, L. A., & Sheather, S. J. (2013) On sensitivity of inverse response plot estimation and the benefits of a robust estimation approach. Scandinavian Journal of Statistics, 40(2), 219-237.

Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley, Chapter 7.

### See Also

`inverseResponsePlot`,`optimize`

### Examples

    with(UN, invTranPlot(ppgdp, infantMortality))
    with(UN, invTranEstimate(ppgdp, infantMortality))

[Package car version 3.0-2 Index]