Box-Tidwell Transformations
Computes the Box-Tidwell power transformations of the predictors in a linear model.
boxTidwell(y, ...)
## S3 method for class 'formula'
boxTidwell(formula, other.x=NULL, data=NULL, subset,
na.action=getOption("na.action"), verbose=FALSE, tol=0.001,
max.iter=25, ...)
## Default S3 method:
boxTidwell(y, x1, x2=NULL, max.iter=25, tol=0.001,
verbose=FALSE, ...)
## S3 method for class 'boxTidwell'
print(x, digits=getOption("digits") - 2, ...)formula |
two-sided formula, the right-hand-side of which gives the predictors to be transformed. |
other.x |
one-sided formula giving the predictors that are not candidates for transformation, including (e.g.) factors. |
data |
an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which
|
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function that indicates what should happen when the data contain |
verbose |
if |
tol |
if the maximum relative change in coefficients is less than |
max.iter |
maximum number of iterations. |
y |
response variable. |
x1 |
matrix of predictors to transform. |
x2 |
matrix of predictors that are not candidates for transformation. |
... |
not for the user. |
x |
|
digits |
number of digits for rounding. |
The maximum-likelihood estimates of the transformation parameters are computed by Box and Tidwell's (1962) method, which is usually more efficient than using a general nonlinear least-squares routine for this problem. Score tests for the transformations are also reported.
an object of class boxTidwell, which is normally just printed.
John Fox jfox@mcmaster.ca
Box, G. E. P. and Tidwell, P. W. (1962) Transformation of the independent variables. Technometrics 4, 531-550.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
boxTidwell(prestige ~ income + education, ~ type + poly(women, 2), data=Prestige)
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