boxcoxfit | R Documentation |
Parameter estimation and plotting of the results for the Box-Cox transformed normal distribution.
boxcoxfit(object, xmat, lambda, lambda2 = NULL, add.to.data = 0, ...)
## S3 method for class 'boxcoxfit'
print(x, ...)
## S3 method for class 'boxcoxfit'
plot(x, hist = TRUE, data = eval(x$call$object), ...)
## S3 method for class 'boxcoxfit'
lines(x, data = eval(x$call$object), ...)
object |
a vector with the data. |
xmat |
a matrix with covariates values. Defaults to |
lambda |
numerical value(s) for the transformation parameter
|
lambda2 |
logical or numerical value(s) of the additional transformation
(see DETAILS below). Defaults to |
add.to.data |
a constant value to be added to the data. |
x |
a list, typically an output of the function
|
hist |
logical indicating whether histograms should to be plotted. |
data |
data values. |
... |
extra parameters to be passed to the minimization
function |
The functions returns the following results:
boxcoxfit |
a list with estimated parameters and results on the numerical minimization. |
print.boxcoxfit |
print estimated parameters. No values returned. |
plot.boxcoxfit |
plots histogram of the data (optional) and
the model. No values returned. This function is only valid if
covariates are not included in |
lines.boxcoxfit |
adds a line with the fitted model to the
current plot. No values returned. This function is only valid if
covariates are not included in |
Paulo Justiniano Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Box, G.E.P. and Cox, D.R.(1964) An analysis of transformations. JRSS B 26:211–246.
rboxcox
and dboxcox
for the
expression and more on the Box-Cox transformation.
Parameter(s) are estimated using the minimization function optim
.
Other packages have BoxCox related functions such as boxcox
in the package MASS and
the function box.cox
in the package ‘car’.
set.seed(384)
## Simulating data
simul <- rboxcox(100, lambda=0.5, mean=10, sd=2)
## Finding the ML estimates
ml <- boxcoxfit(simul)
ml
## Ploting histogram and fitted model
plot(ml)
##
## Comparing models with different lambdas,
## zero means and unit variances
curve(dboxcox(x, lambda=-1), 0, 8)
for(lambda in seq(-.5, 1.5, by=0.5))
curve(dboxcox(x, lambda), 0, 8, add = TRUE)
##
## Another example, now estimating lambda2
##
simul <- rboxcox(100, lambda=0.5, mean=10, sd=2)
ml <- boxcoxfit(simul, lambda2 = TRUE)
ml
plot(ml)
##
## An example with a regression model
##
boxcoxfit(object = trees[,3], xmat = trees[,1:2])
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.