Description Usage Arguments Details Value Author(s) References See Also Examples
Estimate the B-spline coefficients for a regression quantile smoothing spline with optional constraints, using Ng(1996)'s algorithm.
1 2 3 4 5 6 7 8 9 | drqssbc(x, y, w= rep(1,n), pw, knots, degree, Tlambda, constraint,
n.sub = n1000cut(nrq),
equal, smaller, greater, gradient, coef, maxiter = 20 * n,
trace = 1,
n.equal = nrow(equal), n.smaller = nrow(smaller),
n.greater = nrow(greater), n.gradient = nrow(gradient),
nrq = length(x), nl1, neqc, niqc, nvar, nj0,
tau = 0.5, lam, tmin, kmax, lstart, factor,
eps = .Machine$double.eps, print.warn)
|
x |
numeric vector, sorted increasingly, the abscissa values |
y |
numeric, same length as |
w |
numeric vector of weights, same length as |
pw |
penalty weights vector passed to |
knots |
numeric vector of knots for the splines. |
degree |
integer, must be 1 or 2. |
Tlambda |
vector of smoothing parameter values λ; if it is longer than one, an “optimal” value will be selected from these. |
constraint |
see |
n.sub |
integer, not larger than sample size |
equal,smaller, greater |
3-column matrices specifying the respective constraints. The has 0 zeros if there no constraints of the corresponding kind. |
gradient |
3-column matrix for gradient constraints. |
coef |
numeric vector, the initial guess for the B-spline coefficients. |
maxiter |
upper bound of the number of iteration; default to 20*n. |
trace |
integer or logical indicating the tracing level of the underlying algorithms; not much implemented (due to lack of trace in quantreg ..). |
n.equal,n.smaller,n.greater,n.gradient |
integers, each specifying the corresponding number of constraints. |
nrq |
integer, = n, the number of observations. |
nl1 |
integer, number of observations in the l1 norm that correspond to roughness measure (may be zero). |
neqc |
integer giving the number of equations. |
niqc |
integer giving the number of inequality
constraints; of the same length as |
nvar |
integer giving the number of equations and constraints. |
nj0 |
integer; upper limit for the number of unique tau or lambda solutions. |
tau |
desired quantile level; defaults to 0.5 (median). |
lam |
initial λ value. |
tmin |
smallest value of tau to begin PLP in tau. |
kmax |
integer k_0, the largest effective dimension of the model allowed during PLP in lambda. |
lstart |
number, see |
factor |
number in [1,4], see |
eps |
tolerance used in the fortran code in many different contexts. |
print.warn |
logical indicating if warnings should be printed, when the algorithm seems to have behaved somewhat unexpectedly. |
This is an auxiliary function for cobs
, possibly
interesting on its own. This documentation is currently sparse; read
the source code!
a list with components ......
(use the cobs package instead!)
Pin Ng; this help page: Martin Maechler.
Ng, P. (1996) An Algorithm for Quantile Smoothing Splines, Computational Statistics \& Data Analysis 22, 99–118.
The main function cobs
and its auxiliary
qbsks
which calls drqssbc()
repeatedly.
1 2 3 4 5 |
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