Description Usage Arguments Value Author(s) References See Also Examples
Parameter estimation and plotting of the results for the Box-Cox transformed normal distribution.
1 2 3 4 5 6 7 8 9 10 |
object |
a vector with the data. |
xmat |
a matrix with covariates values. Defaults to |
lambda |
numerical value(s) for the transformation parameter lambda. Used as the initial value in the function for parameter estimation. If not provided default values are assumed. If multiple values are passed the one with highest likelihood is used as initial value. |
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,
the minimization function optim
,
the function boxcox
in the package MASS and
the function box.cox
in the package car.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | 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])
|
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