boxCox | R Documentation |
Computes and optionally plots profile log-likelihoods for the parameter of the
Box-Cox power family, the Yeo-Johnson power family, or for either of the parameters in a bcnPower family. This is a slight generalization of the
boxcox
function in the MASS package that allows for families of
transformations other than the Box-Cox power family. the boxCox2d
function
produces a contour
plot of the two-dimensional likelihood profile for the bcnPower family.
boxCox(object, ...)
## Default S3 method:
boxCox(object,
lambda = seq(-2, 2, 1/10), plotit = TRUE,
interp = plotit, eps = 1/50,
xlab=NULL, ylab=NULL, main= "Profile Log-likelihood",
family="bcPower",
param=c("lambda", "gamma"), gamma=NULL,
grid=TRUE, ...)
## S3 method for class 'formula'
boxCox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, family = "bcPower",
param = c("lambda", "gamma"), gamma = NULL, grid = TRUE,
...)
## S3 method for class 'lm'
boxCox(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, ...)
boxCox2d(x, ksds = 4, levels = c(0.5, 0.95, 0.99, 0.999),
main = "bcnPower Log-likelihood", grid=TRUE, ...)
object |
a formula or fitted model object of class |
lambda |
vector of values of |
plotit |
logical which controls whether the result should be plotted; default |
interp |
logical which controls whether spline interpolation is used. Default to
|
eps |
Tolerance for lambda = 0; defaults to 0.02. |
xlab |
defaults to |
ylab |
defaults to |
family |
Defaults to |
param |
Relevant only to |
gamma |
For use when the |
grid |
If TRUE, the default, a light-gray background grid is put on the graph. |
... |
additional arguments passed to |
x |
An object created by a call to |
ksds |
Contour plotting of the log-likelihood surface will cover plus of minus |
levels |
Contours will be drawn at the values of levels. For example, |
main |
Title for the contour plot or the profile log-likelihood plot |
The boxCox
function is an elaboration of the boxcox
function in the
MASS package. The first 7 arguments are the same as in boxcox
, and if the argument family="bcPower"
is used, the result is essentially identical to the function in MASS. Two additional families are the yjPower
and bcnPower
families that allow a few values of the response to be non-positive.
The bcnPower family has two parameters: a power \lambda
and a start or location parameter \gamma
, and the boxCox
function can be used to obtain a profile log-likelihood for either parameter with \lambda
as the default. Alternatively, the boxCox2d
function can be used to get a contour plot of the profile log-likelihood.
Both functions ae designed for their side effects of drawing a graph. The boxCox
function returns a list of the lambda (or possibly, gamma) vector and the computed profile log-likelihood vector,
invisibly if the result is plotted. If plotit=TRUE
plots log-likelihood vs
lambda and indicates a 95% confidence interval about the maximum observed value of
lambda. If interp=TRUE
, spline interpolation is used to give a smoother plot.
Sanford Weisberg, <sandy@umn.edu>
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. (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.
Hawkins, D. and Weisberg, S. (2017) Combining the Box-Cox Power and Generalized Log Transformations to Accomodate Nonpositive Responses In Linear and Mixed-Effects Linear Models South African Statistics Journal, 51, 317-328.
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.
boxcox
, yjPower
, bcPower
, bcnPower
,
powerTransform
, contour
with(trees, boxCox(Volume ~ log(Height) + log(Girth), data = trees,
lambda = seq(-0.25, 0.25, length = 10)))
data("quine", package = "MASS")
with(quine, boxCox(Days ~ Eth*Sex*Age*Lrn,
lambda = seq(-0.05, 0.45, len = 20), family="yjPower"))
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