Logspline Density Estimation
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.
dlogspline(q, fit) plogspline(q, fit) qlogspline(p, fit) rlogspline(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 (dlogspline), probabilities (plogspline), quantiles (qlogspline),
or a random sample (rlogspline) from a logspline density that was fitted using
knot addition and 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 <- 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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