invTranPlot
draws a two-dimensional scatterplot of $Y$ versus
$X$, along with the OLS
fit from the regression of $Y$ on
$(X^{\lambda}-1)/\lambda$. invTranEstimate
finds the nonlinear least squares estimate of $\lambda$ and its
standard error.invTranPlot(x, ...)
## S3 method for class 'formula':
invTranPlot(x, data, subset, na.action, ...)
## S3 method for class 'default':
invTranPlot(x, y, lambda=c(-1, 0, 1), robust=FALSE,
lty.lines=rep(c("solid", "dashed", "dotdash", "longdash", "twodash"),
length=1 + length(lambda)), lwd.lines=2,
col=palette()[1], col.lines=palette(),
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
family="bcPower", optimal=TRUE, key="auto",
id.method = "x",
labels,
id.n = if(id.method[1]=="identify") Inf else 0,
id.cex=1, id.col=palette()[1], grid=TRUE, ...)
invTranEstimate(x, y, family="bcPower", confidence=0.95, robust=FALSE)
lm
, select a subset of the caseslm
, the action for missing dataFALSE
."bcPower"
,
"yjPower"
, or a user-defined family.FALSE
, or if robust=TRUE
,
no interval is returned.pch
argument, rather than colors.palette
"auto"
, in which case a legend is added to
the plot, either above the top marign or in the bottom right or top right corner.
Set to NULL to suppress the legend.id.n=0
for labeling no points. See
showLabels
for details of these arguments.pch
.invTranPlot
plots a graph and returns a data frame with $\lambda$ in the
first column, and the residual sum of squares from the regression
for that $\lambda$ in the second column.
invTranEstimate
returns a list with elements lambda
for the
estimate, se
for its standard error, and RSS
, the minimum
value of the residual sum of squares.inverseResponsePlot
,optimize
with(UN, invTranPlot(gdp, infant.mortality))
with(UN, invTranEstimate(gdp, infant.mortality))
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