Description Usage Arguments Details Value Author(s) References See Also Examples
Nonparametric multivalued regression based on the modes of conditional density estimates.
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x 
Numerical vector: the conditioning variable. 
y 
Numerical vector: the response variable. 
xfix 
Numerical vector corresponding to the input values of which the fitted values shall be calculated. 
a 
Optional bandwidth in xdirection. 
b 
Optional bandwidth in ydirection. 
deg 
Degree of local polynomial used in estimation (0 or 1). 
iter 
Positive integer giving the number of mean shift iterations per point and branch. 
P 
Maximal number of branches. 
start 
Character determining how the starting points are selected.

prun 
Boolean. If TRUE, parts of branches are dismissed (in the
plotted output) where their associated kernel density value falls below the
threshold 
prun.const 
Numerical value giving the constant used above (the higher, the less pruning) 
plot.type 
Vector with two elements. The first one is
charactervalued, with possible values 
labels 
Vector of three character strings. The first one is the "main" title of the graphical output, the second one is the label of the x axis, and the third one the label of the y axis. 
pch 
Plotting character. The default corresponds to small bullets. 
... 
Other arguments passed to 
Computes multimodal nonparametric regression curves based on the maxima of
conditional density estimates. The tool for the estimation is the
conditional mean shift as outlined in Einbeck and Tutz (2006). Estimates of
the conditional modes might fluctuate highly if deg=1
. Hence,
deg=0
is recommended. For bandwidth selection, the hybrid rule
introduced by Bashtannyk and Hyndman (2001) is employed if deg=0
.
This corresponds to the setting method=1
in function
cde.bandwidths
. For deg=1
automatic bandwidth selection is not
supported.
A list with the following components:
xfix 
Grid of predictor values at which the fitted values are calculated. 
fitted.values 
A

bandwidths 
A vector with
bandwidths 
density 
A 
threshold 
The pruning threshold. 
Jochen Einbeck (2007)
Einbeck, J., and Tutz, G. (2006) "Modelling beyond regression functions: an application of multimodal regression to speedflow data". Journal of the Royal Statistical Society, Series C (Applied Statistics), 55, 461475.
Bashtannyk, D.M., and Hyndman, R.J. (2001) "Bandwidth selection for kernel conditional density estimation". Computational Statistics and Data Analysis, 36(3), 279298.
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