powerTransform {car} | R Documentation

## Finding Univariate or Multivariate Power Transformations

### Description

`powerTransform` uses the maximum likelihood-like approach of Box and Cox (1964) to select a transformatiion of a univariate or multivariate response for normality, linearity and/or constant variance. Available families are the default Box-Cox power family, and the Yeo-Johnson and skew power familes that may be useful when a response is not strictly positive. `powerTransform` passes information to `estimateTransform`, and only the former will be of interest for most users.

### Usage

    powerTransform(object, ...)

    ## Default S3 method:
    powerTransform(object, family="bcPower", ...)

    ## S3 method for class 'lm'
    powerTransform(object, family="bcPower", ...)

    ## S3 method for class 'formula'
    powerTransform(object, data, subset, weights, na.action, family="bcPower",
      ...)

    estimateTransform(X, Y, weights=NULL, family="bcPower", start=NULL,
             method="L-BFGS-B", ...)

    ## Default S3 method:
    estimateTransform(X, Y, weights=NULL, family="bcPower", start=NULL,
             method="L-BFGS-B", ...)

    ## S3 method for class 'skewPower'
    estimateTransform(X, Y, weights=NULL, ...)

### Arguments

`object` | This can either be an object of class `lm`, a formula, or a matrix or vector; see below.
`data` | A data frame or environment, as in `lm`.
`subset` | Case indices to be used, as in `lm`.
`weights` | Weights as in `lm`.
`na.action` | Missing value action, as in ‘lm’.
`family` | The quoted name of a family of transformations. The available options are `"bcPower"` the default for the Box-Cox power family; `"yjpower"` for the Yeo-Johnson family, and `"skewPower"` for the two-parameter skew power family. The families are documented at `bcPower` and `skewPower`.
`...` | Additional arguments that are passed to `estimateTransform` which does the actual computing, or to the `optim` function, which does the maximization.
`X` | A matrix or data.frame giving the “right-side variables”, including a column of ones if the intercept is present.
`Y` | A vector or matrix or data.frame giving the “left-side variables.”
`start` | Starting values for the computations. The default value of NULL is usually adequate.
`method` | The computing alogrithm used by `optim` for the maximization. The default `"L-BFGS-B"` appears to work well.

### Details

The function `powerTransform` is used to estimate normalizing/linearizing/variance stabilizing transformations of a univariate or a multivariate response in a linear regression. For a univariate response, a formula like `z~x1+x2+x3` will estimate a transformation for the response `z` from a family of transformations indexed by one parameter for Box-Cox and Yeo-Johnson transformations, or two parameters for the skew power family, that makes the residuals from the regression of the transformed `z` on the predictors as closed to normally distributed as possible.

For a formula like `cbind(y1,y2,y3)~x1+x2+x3`, the three variables on the left-side are all transformed, generally with different transformations to make all the residuals as close to normally distributed as possible. This is not the same as three univariate transformations becuase the variables transformed are allowed to be correlated. `cbind(y1,y2,y3)~1` would specify transformations to multivariate normality with no predictors. This generalizes the multivariate power transformations suggested by Velilla (1993) by allowing for different families of transformations, and by allowing for predictors. Cook and Weisberg (1999) and Weisberg (2014) suggest the usefulness of transforming a set of predictors `z1, z2, z3` for multivariate normality and for transforming for multivariate normality conditional on levels of a factor, which is equivalent to setting the predictors to be indicator variables for that factor.

Specifying the first argument as a vector, for example `powerTransform(ais$LBM)`, is equivalent to `powerTransform(LBM ~ 1, ais)`. Similarly, `powerTransform(cbind(ais$LBM, ais$SSF))`, where the first argument is a matrix rather than a formula is equivalent to specification of a mulitvariate linear model `powerTransform(cbind(LBM, SSF) ~ 1, ais)`.

Three families of power transformations are available. The Box-Cox pwoer family of power transformations, `family="bcPower"`, equals (U^(lambda)-1)/lambda for lambda not equal to 0, and log(U) if lambda = 0. A scaled version of this transformation is used in computing with all the families to make the Jacobian of the transformation equal to 1.

If `family="yjPower"` then the Yeo-Johnson transformations are used. This is is Box-Cox transformation of U+1 for nonnegative values, and of |U|+1 with parameter 2-lambda for U negative.

If `family="skewPower"` then the skew power family of transformations suggested by Hawkins and Weisberg (2015) is used. This is a two-parameter family that would generally be applied with a response with occasional negative values; see `skewPower` for the details and examples. This family has a power parameter lambda and a non-negative start parameter gamma, with gamma=0 equal to the Box-Cox transformation.

The function `testTransform` is used to obtain likelihood ratio tests for any specified value for the transformation parameter(s).

### Value

An object of class `powerTransform` or class `skewpowerTransform` if `family="skewPower"` that inherits from `powerTransfrom` is returned, including the components listed below.

Several methods are available for use with `powerTransform` objects. The `coef` method returns the estimated transformation parameters, while `coef(object, round=TRUE)` will return the transformations rounded to nearby convenient values within 1.96 standard errors of the mle, if any exist. The `vcov` function returns the estimated covariance matrix of the estimated transformation parameters. A `print` method is used to print the estimates and `summary` method provides more information including likelihood ratio type tests that all power parameters equal one and that all transformation parameters equal zero, for log transformations, and for a convenient rounded value not far from the mle. In the case of the skew power family, these tests are based on the profile log-likelihood obtained by maximizing over the start parameter, thus treating the start as a nusiance parameter of lesser interest than the pwoer parameter. `testTransform` can be called directly to test any other value for lambda or for skew power lambda and gamma. There is a `plot.powerTransform` method for plotting the transformed values, and also a `contour.skewpowerTransform` method to obtain a contour plot of the two-dimensional log-likelihood for the skew power parameters when the response in univariate. Finally, the `boxCox` method can be used to plot the univariate log-likleihood for the Box-Cox or Yeo-Johnson power families, or the profile log-likelihood of each of the parameters in the skew power family.

The components of the returned object are

`value` | The value of the loglikelihood at the mle.
`counts` | See `optim`.
`convergence` | See `optim`.
`message` | See `optim`.
`hessian` | The hessian matrix.
`start` | Starting values for the computations.
`lambda` | The ml estimate for the power parameter
`gamma` | The ml estimate for the start parameter for the skew power family
`roundlam` | Convenient rounded values for the estimates. These rounded values will often be the desirable transformations.
`family` | The transformation family
`xqr` | QR decomposition of the predictor matrix.
`y` | The responses to be transformed
`x` | The predictors
`weights` | The weights if weighted least squares.

### Author(s)

Sanford Weisberg, <sandy@umn.edu>

### References

Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. Journal of the Royal Statisistical Society, Series B. 26 211-46.

Cook, R. D. and Weisberg, S. (1999) Applied Regression Including Computing and Graphics. Wiley.

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

Hawkins, D. and Weisberg, S. (2015) Combining the Box-Cox Power and Genralized Log Transformations to Accomodate Negative Responses, submitted for publication.

Velilla, S. (1993) A note on the multivariate Box-Cox transformation to normality. Statistics and Probability Letters, 17, 259-263.

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

Yeo, I. and Johnson, R. (2000) A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954-959.

### See Also

`testTransform`, `optim`, `bcPower`, `skewPower`, `transform`, `boxCox`. Documentation for `skewPower` includes examples of the use of the skew power family.

### Examples

    # Box Cox Method, univariate
    summary(p1 <- powerTransform(cycles ~ len + amp + load, Wool))

    # fit linear model with transformed response:
    coef(p1, round=TRUE)
    summary(m1 <- lm(bcPower(cycles, p1$roundlam) ~ len + amp + load, Wool))

    # Multivariate Box Cox
    summary(powerTransform(cbind(len, ADT, trks, sigs1) ~ 1, Highway1))

    # Multivariate transformation to normality within levels of 'hwy'
    summary(a3 <- powerTransform(cbind(len, ADT, trks, sigs1) ~ hwy, Highway1))

    # test lambda = (0 0 0 -1)
    testTransform(a3, c(0, 0, 0, -1))

    # save the rounded transformed values, plot them with a separate
    # color for each highway type
    transformedY <- bcPower(with(Highway1, cbind(len, ADT, trks, sigs1)),
                    coef(a3, round=TRUE))
    ## Not run: pairs(transformedY, col=as.numeric(Highway1$hwy))

[Package car version 2.1-2 Index]