Description Usage Arguments Details Value Author(s) References Examples
Computes the BoxTidwell power transformations of the predictors in a linear model.
1 2 3 4 5 6 7 8 9 10 11 12 13  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 
twosided formula, the righthandside of which gives the predictors to be transformed. 
other.x 
onesided 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 maximumlikelihood estimates of the transformation parameters are computed by Box and Tidwell's (1962) method, which is usually more efficient than using a general nonlinear leastsquares routine for this problem. Score tests for the transformations are also reported.
an object of class boxTidwell
, which is normally just printed.
John Fox [email protected]
Box, G. E. P. and Tidwell, P. W. (1962) Transformation of the independent variables. Technometrics 4, 531550.
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.
1  boxTidwell(prestige ~ income + education, ~ type + poly(women, 2), data=Prestige)

Score Statistic pvalue MLE of lambda
income 4.482406 0.0000074 0.3476283
education 0.216991 0.8282154 1.2538274
iterations = 8
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