| SmoothedSplineDistribution {fBasics} | R Documentation |
A collection and description of functions to compute
density, distribution function, quantile function
and to generate random variates for empirical
distributions. Estimates are done using smoothing
spline ANOVA models with cubic spline, linear spline,
or thin-plate spline marginals for numerical variables.
The functions are:
dssd | Spline smoothed density, |
pssd | spline smoothed probability function, |
qssd | spline smoothed quantiles, |
rssd | random deviates drawn from a ssd. |
dssd(x, param) pssd(q, param) qssd(p, param) rssd(n, param)
n |
number of observations. |
p |
a numeric vector of probabilities. |
x, q |
a numeric vector of quantiles. |
param |
a S3 object specifying the parameters as
returned by the function ssdFit.
|
This is an easy to use version for the functions implemented in Chong Gu's contributed R package "gss" which is downloadable from the CRAN server. If you require more functionality, e.g. to tailor the parameter estimate we recommend to install Gu's package. The installation does not interfere with the functions implemented in Rmetrics.
Note, before you can use the functions you have to estimate the
parameters param using the function ssdFit.
All values are numeric vectors:
d* returns the density,
p* returns the distribution function,
q* returns the quantile function, and
r* generates random deviates.
The functions do not implement the full functionality provided by R's contributed package "gss". Only the "cubic" spline method is provided and most of the optional arguments are set to default values. Since the original "gss" package does not interfere with Rmetrics you can load it in parallel.
It's worth to note that the "gss" package does not work under SPlus,
but the modified and adapted functions ssdFit and *ssd
can be used.
Chong Gu for the code from R's contributed package 'gss',
Diethelm Wuertz for the Rmetrics R-port.
Gu C., Wang, J. (2003); emph{Penalized likelihood density estimation: Direct cross-validation and scalable approximation}, Statistica Sinica, 13, 811–826.
Gu C. (2002); Smoothing Spline ANOVA Models, New York, Springer-Verlag.
## SOURCE("fBasics.13C-SmoothedSplineDistribution")
## ssd -
## Not run:
xmpBasics("\nStart: Spline Smoothed Distribution > ")
par(mfcol = c(2, 1), cex = 0.5)
set.seed(1953)
x = rnorm(1000)
param = ssdFit(x)
# Plot empirical density and compare with fitted density:
hist(x, n = 25, probability = TRUE, border = "white", col = "steelblue4")
s = seq(min(x), max(x), 0.1)
lines(s, dssd(s, param), lwd = 2)
# Plot df and compare with true df:
plot(sort(x), (1:1000/1000), main = "Probability", col = "steelblue4")
lines(s, pssd(s, param), lwd = 2)
# Compute quantiles:
qssd(pssd(seq(-3, 3, 1), param), param)
## End(Not run)