lambdaPlot {BHH2} | R Documentation |
Trace regression coefficients' t-values or F-ratios for different values of λ in the Box-Cox transformation.
lambdaPlot(mod, lambda = seq(-1, 1, by = 0.1), stat = "F", global = TRUE, cex = par("cex"), ...)
mod |
list. A list of class lm . |
lambda |
numeric. The values of λ in the Box-Cox transformation. See Details. |
stat |
character. Either "t" of "F" , corresponding to the
coefficients' t-values or F-ratios to display. |
global |
logical. Applied only for stat="F" , if TRUE ,
the model's F-ratio is traced, otherwise the coefficients' F-statistics. |
cex |
numeric. Expansion factor used to label the trace lines.par("cex") bu default. |
... |
additional graphical parameters passed to plot function. |
The response is transformed as
Y=(y^λ - 1)/λ
for each value of λ (lambda
) and the model refitted.
The t-values or F-ratios of the coefficients are saved for the display.
If global=TRUE
, then the F-ratio of the whole model is
plotted instead.
The function returns an invisible list with components:
lambda |
numeric. Vector of length m with the
different values of λ. |
t.lambda |
matrix (k x m ), where m
is the number of coefficients in model mod without the intercept,
with the coefficient's t-values. |
f.lambda |
matrix (k x m ) with the coefficient's F-values.
if global = FALSE , otherwise the matrix is (1 x m ), with the
corresponding model F-ratio. |
For each value of λ the model is refitted. Computations can be done more efficiently and will be incorporated in future versions.
Ernesto Barrios
Box, G. E. P. and C. Fung (1995) "The Importance of Data Transformation in Designed Experiments for Life Testing". Quality Engineering, Vol. 7, No. 3, pp. 625-68.
Box G. E. P, Hunter, J. S. and Hunter, W. C. (2005). Statistics for Experimenters II. New York: Wiley.
library(BHH2) # Lambda Plot tracing t values. data(woolen.data) woolen.lm <- lm(y~x1+x2+x3+I(x1^2)+I(x2^2)+I(x3^2)+ I(x1*x2)+I(x1*x3)+I(x2*x3)+I(x1*x2*x3),data=woolen.data) lambdaPlot(woolen.lm,cex=.8,stat="t") # Lambda Plot tracing F values. woolen2.lm <- lm(y~x1+x2+x3,data=woolen.data) lambdaPlot(woolen2.lm,lambda=seq(-1,1,length=41),stat="F",global=TRUE) # Lambda Plot tracing F values. data(poison.data) poison.lm <- lm(y~treat*poison,data=poison.data) lambdaPlot(poison.lm,lambda=seq(-3,1,by=.1),stat="F",global=FALSE)