# dist-ssd: Spline Smoothed Distribution In fBasics: Rmetrics - Markets and Basic Statistics

## Description

Density, distribution function, quantile function and random generation from smoothing spline estimates.

## Usage

 ```1 2 3 4``` ```dssd(x, param, log = FALSE) pssd(q, param) qssd(p, param) rssd(n, param) ```

## Arguments

 `param` an object as returned by the function `ssdFit`.. `log` a logical flag by default `FALSE`. Should labels and a main title drawn to the plot? `n` number of observations. `p` a numeric vector of probabilities. `x, q` a numeric vector of quantiles.

## Value

All values for the `*ssd` functions are numeric vectors: `d*` returns the density, `p*` returns the distribution function, `q*` returns the quantile function, and `r*` generates random deviates.

All values have attributes named `"param"` listing the values of the distributional parameters.

## Author(s)

Diethelm Wuertz, Chong Gu for the underlying `gss` package.

## References

Gu, C. (2002), Smoothing Spline ANOVA Models, New York Springer–Verlag.

Gu, C. and Wang, J. (2003), Penalized likelihood density estimation: Direct cross-validation and scalable approximation, Statistica Sinica, 13, 811–826.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ``` ## ssdFit - set.seed(1953) r = rnorm(500) hist(r, breaks = "FD", probability = TRUE, col = "steelblue", border = "white") ## ssdFit - param = ssdFit(r) ## dssd - u = seq(min(r), max(r), len = 301) v = dssd(u, param) lines(u, v, col = "orange", lwd = 2) ```

fBasics documentation built on Nov. 18, 2017, 4:05 a.m.