polspline: logspline – R documentation

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logspline

Logspline Density Estimation

Description

Fits a logspline density using splines to approximate the log-density using the 1997 knot addition and deletion algorithm (logspline). The 1992 algorithm is available using the oldlogspline function.

Usage

logspline(x, lbound, ubound, maxknots = 0, knots, nknots = 0, penalty,
silent = TRUE, mind = -1, error.action = 2)

Arguments

`x`	data vector. The data needs to be uncensored. `oldlogspline` can deal with right- left- and interval-censored data.
`lbound,ubound`	lower/upper bound for the support of the density. For example, if there is a priori knowledge that the density equals zero to the left of 0, and has a discontinuity at 0, the user could specify `lbound = 0`. However, if the density is essentially zero near 0, one does not need to specify `lbound`.
`maxknots`	the maximum number of knots. The routine stops adding knots when this number of knots is reached. The method has an automatic rule for selecting maxknots if this parameter is not specified.
`knots`	ordered vector of values (that should cover the complete range of the observations), which forces the method to start with these knots. Overrules knots. If `knots` is not specified, a default knot-placement rule is employed.
`nknots`	forces the method to start with `nknots` knots. The method has an automatic rule for selecting `nknots` if this parameter is not specified.
`penalty`	the parameter to be used in the AIC criterion. The method chooses the number of knots that minimizes `-2 * loglikelihood + penalty * (number of knots - 1)`. The default is to use a penalty parameter of `penalty = log(samplesize)` as in BIC. The effect of this parameter is summarized in `summary.logspline`.
`silent`	should diagnostic output be printed?
`mind`	minimum distance, in order statistics, between knots.
`error.action`	how should `logspline` deal with non-convergence problems? Very-very rarely in some extreme situations `logspline` has convergence problems. The only two situations that I am aware of are when there is effectively a sharp bound, but this bound was not specified, or when the data is severly rounded. `logspline` can deal with this in three ways. If `error.action` is 2, the same data is rerun with the slightly more stable, but less flexible `oldlogspline`. The object is translated in a `logspline` object using `oldlogspline.to.logspline`, so this is almost invisible to the user. It is particularly useful when you run simulation studies, as he code can seemlessly continue. Only the `lbound` and `ubound` options are passed on to `oldlogspline`, other options revert to the default. If `error.action` is 1, a warning is printed, and `logspline` returns nothing (but does not crash). This is useful if you run a simulation, but do not like to revert to `oldlogspline`. If `error.action` is 0, the code crashes using the `stop` function.

Value

Object of the class logspline, that is intended as input for plot.logspline (summary plots), summary.logspline (fitting summary), dlogspline (densities), plogspline (probabilities), qlogspline (quantiles), rlogspline (random numbers from the fitted distribution).

The object has the following members:

`call`	the command that was executed.
`nknots`	the number of knots in the model that was selected.
`coef.pol`	coefficients of the polynomial part of the spline. The first coefficient is the constant term and the second is the linear term.
`coef.kts`	coefficients of the knots part of the spline. The `k`-th element is the coefficient of (x-t(k))^3_+ (where x^3_+ means the positive part of the third power of x, and t(k) means knot `k`).
`knots`	vector of the locations of the knots in the `logspline` model.
`maxknots`	the largest number of knots minus one considered during fitting (i.e. with `maxknots = 6` the maximum number of knots is 5).
`penalty`	the penalty that was used.
`bound`	first element: 0 - `lbound` was -infinity, 1 it was something else; second element: `lbound`, if specified; third element: 0 - `ubound` was infinity, 1 it was something else; fourth element: `ubound`, if specified.
`samples`	the sample size.
`logl`	matrix with 3 columns. Column one: number of knots; column two: model fitted during addition (1) or deletion (2); column 3: log-likelihood.
`range`	range of the input data.
`mind`	minimum distance in order statistics between knots required during fitting (the actual minimum distance may be much larger).

Author(s)

Charles Kooperberg clk@fredhutch.org.

References

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.

Examples

y <- rnorm(100)
fit <- logspline(y)       
plot(fit)
#
# as (4 == length(-2, -1, 0, 1, 2) -1), this forces these initial knots,
# and does no knot selection
fit <- logspline(y, knots = c(-2, -1, 0, 1, 2), maxknots = 4, penalty = 0)  
#
# the following example give one of the rare examples where logspline
# crashes, and this shows the use of error.action = 2.
#
set.seed(118)
zz <- rnorm(300)
zz[151:300] <- zz[151:300]+5
zz <- round(zz)
fit <- logspline(zz)
#
# you could rerun this with 
# fit <- logspline(zz, error.action=0)
# or
# fit <- logspline(zz, error.action=1)

polspline

Polynomial Spline Routines

v1.1.19

GPL (>= 2)

Authors

Charles Kooperberg [aut, cre], Cleve Moler [ctb] (LINPACK routines in src), Jack Dongarra [ctb] (LINPACK routines in src)

Initial release

2020-05-15