R: Logspline Density Estimation - 1992 version

doldlogspline {logspline} | R Documentation

## Logspline Density Estimation - 1992 version

### Description

Probability density function (`doldlogspline`), distribution function (`poldlogspline`), quantiles (`qoldlogspline`), and random samples (`roldlogspline`) from a logspline density that was fitted using the 1992 knot deletion algorithm (`oldlogspline`). The 1997 algorithm using knot deletion and addition is available using the `logspline` function.

### Usage

    doldlogspline(q, fit)
    poldlogspline(q, fit)
    qoldlogspline(p, fit)
    roldlogspline(n, fit)

### Arguments

`q` | vector of quantiles. Missing values (NAs) are allowed.
`p` | vector of probabilities. Missing values (NAs) are allowed.
`n` | sample size. If `length(n)` is larger than 1, then `length(n)` random values are returned.
`fit` | `oldlogspline` object, typically the result of `oldlogspline`.

### Details

Elements of `q` or `p` that are missing will cause the corresponding elements of the result to be missing.

### Value

Densities (`doldlogspline`), probabilities (`poldlogspline`), quantiles (`qoldlogspline`), or a random sample (`roldlogspline`) from an `oldlogspline` density that was fitted using knot deletion.

### Author(s)

Charles Kooperberg clk@fredhutch.org.

### References

Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301–328.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371–1470.

### See Also

`logspline`, `oldlogspline`, `plot.oldlogspline`, `summary.oldlogspline`

### Examples

    x <- rnorm(100)
    fit <- oldlogspline(x)
    qq <- qoldlogspline((1:99)/100, fit)
    plot(qnorm((1:99)/100), qq)                  # qq plot of the fitted density
    pp <- poldlogspline((-250:250)/100, fit)
    plot((-250:250)/100, pp, type = "l")
    lines((-250:250)/100, pnorm((-250:250)/100)) # asses the fit of the distribution
    dd <- doldlogspline((-250:250)/100, fit)
    plot((-250:250)/100, dd, type = "l")
    lines((-250:250)/100, dnorm((-250:250)/100)) # asses the fit of the density
    rr <- roldlogspline(100, fit)                # random sample from fit

[Package logspline version 2.1.13 Index]