Density (dlogspline), cumulative probability (plogspline), quantiles
(qlogspline), and random samples (rlogspline) from
a logspline density that was fitted using
the 1997 knot addition and deletion algorithm (logspline).
The 1992 algorithm is available using the oldlogspline 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
logspline object, typically the result of logspline.
Details
Elements of q or p that are missing will cause the
corresponding elements of the result to be missing.
Value
Densities (dlogspline), probabilities (plogspline), quantiles (qlogspline),
or a random sample (rlogspline) from a logspline density that was fitted using
knot addition and 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 <- logspline(x)
qq <- qlogspline((1:99)/100, fit)
plot(qnorm((1:99)/100), qq) # qq plot of the fitted density
pp <- plogspline((-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 <- dlogspline((-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 <- rlogspline(100, fit) # random sample from fit