
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.dlogspline(q, fit)
plogspline(q, fit)
qlogspline(p, fit)
rlogspline(n, fit)
length(n)
is larger than 1, then
length(n)
random values are returned.logspline
object, typically the result of logspline
.dlogspline
), probabilities (plogspline
), quantiles (qlogspline
),
or a random sample (rlogspline
) from a logspline
density that was fitted using
knot addition and deletion.q
or p
that are missing will cause the
corresponding elements of the result to be missing.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.
logspline
,
plot.logspline
,
summary.logspline
,
oldlogspline
.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
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