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.
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.
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