boxcox
function in the MASS package that allows for families of transformations
other than the Box-Cox power family.
boxCox(object, ...)
"boxCox"(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, interp = plotit, eps = 1/50, xlab=NULL, ylab=NULL, family="bcPower", param=c("lambda", "gamma"), gamma=NULL, grid=TRUE, ...)
"boxCox"(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, ...)
"boxCox"(object, lambda = seq(-2, 2, 1/10), plotit = TRUE, ...)
lm
or aov
.
TRUE
.
TRUE
if plotting with lambda of length less than 100.
"lambda"
or "gamma"
.
"log-Likelihood"
or for skewPower family to the appropriate label.
"bcPower"
for the Box-Cox power family of
transformations. If set to "yjPower"
the Yeo-Johnson family, which
permits negative responses, is used. If set to skewPower
the function gives the profile
log-likelihood for the parameter selected via param
.
family="skewPower"
, produces a profile log-likelihood for the parameter selected, maximizing over the remaining parameter.family="skewPower", param="gamma"
. If this is a vector of positive values, then the profile log-likelihood for the location (or start) parameter in the skew power family is evaluated at these values of gamma. If gamma is NULL
, then evaulation is done at 100 equally spaced points between min(.01, gmax - 3*se)
and gmax + 3*se
, where gmax
is the maximimul likelihood estimate of gamma, and se
is its estimated standard error. See skewPower
for the definition of gamma
.
plot
.
plotit=TRUE
plots log-likelihood vs
lambda and indicates a 95
lambda. If interp=TRUE
, spline interpolation is used to give a smoother plot.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 skewPower
families that allow a few values of the response to be non-positive.
The skew power family has two parameters a power $lambda$ and a start or location parameter $gamma$, and this functin can be used to obtain a profile log-likelihood for either parameter.
boxcox
, yjPower
, bcPower
, skewPower
,
powerTransform
boxCox(Volume ~ log(Height) + log(Girth), data = trees,
lambda = seq(-0.25, 0.25, length = 10))
data("quine", package = "MASS")
boxCox(Days ~ Eth*Sex*Age*Lrn, data = quine,
lambda = seq(-0.05, 0.45, len = 20), family="yjPower")
Run the code above in your browser using DataCamp Workspace