Logspline Density Estimation - 1992 version
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.
doldlogspline(q, fit) poldlogspline(q, fit) qoldlogspline(p, fit) roldlogspline(n, fit)
q |
vector of quantiles. Missing values (NAs) are allowed. |
p |
vector of probabilities. Missing values (NAs) are allowed. |
n |
sample size. If |
fit |
|
Elements of q or p that are missing will cause the
corresponding elements of the result to be missing.
Densities (doldlogspline), probabilities (poldlogspline), quantiles (qoldlogspline),
or a random sample (roldlogspline)
from an oldlogspline density that was fitted using
knot deletion.
Charles Kooperberg clk@fredhutch.org.
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.
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
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