Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates kernel conditional density estimate using local polynomial estimation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 
x 
Numerical vector or matrix: the conditioning variable(s). 
y 
Numerical vector: the response variable. 
deg 
Degree of local polynomial used in estimation. 
link 
Link function used in estimation. Default "identity". The other possibility is "log" which is recommended if degree > 0. 
a 
Optional bandwidth in x direction. 
b 
Optional bandwidth in y direction. 
mean 
Estimated mean of yx. If present, it will adjust conditional density to have this mean. 
x.margin 
Values in xspace on which conditional density is
calculated. If not specified, an equispaced grid of 
y.margin 
Values in yspace on which conditional density is
calculated. If not specified, an equispaced grid of 
x.name 
Optional name of x variable used in plots. 
y.name 
Optional name of y variable used in plots. 
use.locfit 
If TRUE, will use 
fw 
If TRUE (default), will use fixed window width estimation. Otherwise nearest neighbourhood estimation is used. If the dimension of x is greater than 1, nearest neighbourhood must be used. 
rescale 
If TRUE (default), will rescale the conditional densities to integrate to one. 
nxmargin 
Number of values used in 
nymargin 
Number of values used in 
a.nndefault 
Default nearest neighbour bandwidth (used only if

... 
Additional arguments are passed to locfit. 
If bandwidths are omitted, they are computed using normal reference rules described in Bashtannyk and Hyndman (2001) and Hyndman and Yao (2002). Bias adjustment uses the method described in Hyndman, Bashtannyk and Grunwald (1996). If deg>1 then estimation is based on the local parametric estimator of Hyndman and Yao (2002).
A list with the following components:
x 
grid in x direction on which density evaluated. Equal to x.margin if specified. 
y 
grid in y direction on which density is evaluated. Equal to y.margin if specified. 
z 
value of conditional density estimate returned as a matrix. 
a 
window width in x direction. 
b 
window width in y direction. 
x.name 
Name of x variable to be used in plots. 
y.name 
Name of y variable to be used in plots. 
Rob J Hyndman
Hyndman, R.J., Bashtannyk, D.M. and Grunwald, G.K. (1996) "Estimating and visualizing conditional densities". Journal of Computational and Graphical Statistics, 5, 315336.
Bashtannyk, D.M., and Hyndman, R.J. (2001) "Bandwidth selection for kernel conditional density estimation". Computational statistics and data analysis, 36(3), 279298.
Hyndman, R.J. and Yao, Q. (2002) "Nonparametric estimation and symmetry tests for conditional density functions". Journal of Nonparametric Statistics, 14(3), 259278.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # Old faithful data
faithful.cde < cde(faithful$waiting, faithful$eruptions,
x.name="Waiting time", y.name="Duration time")
plot(faithful.cde)
plot(faithful.cde, plot.fn="hdr")
# Melbourne maximum temperatures with bias adjustment
x < maxtemp[1:3649]
y < maxtemp[2:3650]
maxtemp.cde < cde(x, y,
x.name="Today's max temperature", y.name="Tomorrow's max temperature")
# Assume linear mean
fit < lm(y~x)
fit.mean < list(x=6:45,y=fit$coef[1]+fit$coef[2]*(6:45))
maxtemp.cde2 < cde(x, y, mean=fit.mean,
x.name="Today's max temperature", y.name="Tomorrow's max temperature")
plot(maxtemp.cde)

This is hdrcde 3.3
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.