dlogspline: Logspline Density Estimation

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dlogsplineR Documentation

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) 



vector of quantiles. Missing values (NAs) are allowed.


vector of probabilities. Missing values (NAs) are allowed.


sample size. If length(n) is larger than 1, then length(n) random values are returned.


logspline object, typically the result of logspline.


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.

See Also

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

polspline documentation built on Nov. 23, 2022, 1:05 a.m.