R: COnstrained B-Splines Nonparametric Regression Quantiles

cobs {cobs}

R Documentation

COnstrained B-Splines Nonparametric Regression Quantiles

Description

Computes constrained quantile curves using linear or quadratic splines. The median spline (L_1 loss) is a robust (constrained) smoother.

Usage

cobs(x, y, constraint = c("none", "increase", "decrease",
                          "convex", "concave", "periodic"),
     w = rep(1,n),
     knots, nknots = if(lambda == 0) 6 else 20,
     method = "quantile", degree = 2, tau = 0.5,
     lambda = 0, ic = c("AIC", "SIC", "BIC", "aic", "sic", "bic"),
     knots.add = FALSE, repeat.delete.add = FALSE, pointwise = NULL,
     keep.data = TRUE, keep.x.ps = TRUE,
     print.warn = TRUE, print.mesg = TRUE, trace = print.mesg,
     lambdaSet = exp(seq(log(lambda.lo), log(lambda.hi), length.out = lambda.length)),
     lambda.lo = f.lambda*1e-4, lambda.hi = f.lambda*1e3, lambda.length = 25,
     maxiter = 100,
     rq.tol = 1e-8, toler.kn = 1e-6, tol.0res = 1e-6, nk.start = 2)

Arguments

`x`	vector of covariate; missing values are omitted.
`y`	vector of response variable. It must have the same length as `x`.
`constraint`	character (string) specifying the kind of constraint; must be one of the values in the default list above; may be abbreviated. More flexible constraints can be specified via the `pointwise` argument (below).
`w`	vector of weights the same length as `x` (y) assigned to both `x` and `y`; default to all weights being one.
`knots`	vector of locations of the knot mesh; if missing, `nknots` number of `knots` will be created using the specified `method` and automatic knot selection will be carried out for regression B-spline (`lambda=0`); if not missing and `length(knots)==nknots`, the provided knot mesh will be used in the fit and no automatic knot selection will be performed; otherwise, automatic knots selection will be performed on the provided `knots`.
`nknots`	maximum number of knots; defaults to 6 for regression B-splines, 20 for smoothing B-splines.
`method`	character string specifying the method for generating `nknots` number of `knots` when `knots` is not provided; either `"quantile"` (equally spaced in percentile levels) or `"uniform"` (equally spaced knots); defaults to "quantile".
`degree`	degree of the splines; 1 for linear spline (equivalent to `L_1`-roughness) and 2 for quadratic spline (corresponding to `L_{\infty}` roughness); defaults to 2.
`tau`	desired quantile level; defaults to 0.5 (median).
`lambda`	penalty parameter `\lambda` `\lambda = 0`: no penalty (regression B-spline); `\lambda > 0`: smoothing B-spline with the given `\lambda`; `\lambda < 0`: smoothing B-spline with `\lambda` chosen by a Schwarz-type information criterion.
`ic`	string indicating the information criterion used in knot deletion and addition for the regression B-spline method, i.e., when `lambda == 0`; `"AIC"` (Akaike-type, equivalently `"aic"`) or `"SIC"` (Schwarz-type, equivalently `"BIC"`, `"sic"` or `"bic"`). Defaults to `"AIC"`. Note that the default was `"SIC"` up to cobs version 1.1-6 (dec.2008).
`knots.add`	logical indicating if an additional step of stepwise knot addition should be performed for regression B-splines.
`repeat.delete.add`	logical indicating if an additional step of stepwise knot deletion should be performed for regression B-splines.
`pointwise`	an optional three-column matrix with each row specifies one of the following constraints: `( 1,xi,yi)`: fitted value at xi will be `\ge` yi; `(-1,xi,yi)`: fitted value at xi will be `\le` yi; `( 0,xi,yi)`: fitted value at xi will be `=` yi; `( 2,xi,yi)`: derivative of the fitted function at xi will be yi.
`keep.data`	logical indicating if the `x` and `y` input vectors after removing `NA`s should be kept in the result.
`keep.x.ps`	logical indicating if the pseudo design matrix `\tilde{X}` should be returned (as sparse matrix). That is needed for interval prediction, `predict.cobs(*, interval=..)`.
`print.warn`	flag for printing of interactive warning messages; true by default; set to `FALSE` if performing simulation.
`print.mesg`	logical flag or integer for printing of intermediate messages; true by default. Probably needs to be set to `FALSE` in simulations.
`trace`	integer `\ge 0` indicating how much diagnostics the low-level code in `drqssbc2` should print; defaults to `print.mesg`.
`lambdaSet`	numeric vector of lambda values to use for grid search; in that case, defaults to a geometric sequence (a “grid in log scale”) from `lambda.lo` to `lambda.hi` of length `lambda.length`.
`lambda.lo`, `lambda.hi`	lower and upper bound of the grid search for lambda (when `lambda < 0`). The defaults are meant to keep everything scale-equivariant and are hence using the factor `f = \sigma_x^d`, i.e., `f.lambda <- sd(x)^degree`. Note however that currently the underlying algorithms in package quantreg are not scale equivariant yet.
`lambda.length`	number of grid points in the grid search for optimal lambda.
`maxiter`	upper bound of the number of iterations; defaults to 100.
`rq.tol`	numeric convergence tolerance for the interior point algorithm called from `rq.fit.sfnc()` or `rq.fit.sfn()`.
`toler.kn`	numeric tolerance for shifting the boundary knots outside; defaults to `10^{-6}`.
`tol.0res`	tolerance for testing `\|r_i\| = 0`, passed to `qbsks2` and `drqssbc2`.
`nk.start`	number of starting knots used in automatic knot selection. Defaults to the minimum of 2 knots.

Details

cobs() computes the constraint quantile smoothing B-spline with penalty when lambda is not zero.
If lambda < 0, an optimal lambda will be chosen using Schwarz type information criterion.
If lambda > 0, the supplied lambda will be used.
If lambda = 0, cobs computes the constraint quantile regression B-spline with no penalty using the provided knots or those selected by Akaike or Schwarz information criterion.

Value

an object of class cobs, a list with components

`call`	the `cobs(..)` call used for creation.
`tau`, `degree`	same as input
`constraint`	as input (but no more abbreviated).
`pointwise`	as input.
`coef`	B-spline coefficients.
`knots`	the final set of knots used in the computation.
`ifl`	exit code := `1 + ierr` and `ierr` is the error from `rq.fit.sfnc` (package quantreg); consequently, `ifl = 1` means “success”; all other values point to algorithmic problems or failures.
`icyc`	length 2: number of cycles taken to achieve convergence for final lambda, and total number of cycles for all lambdas.
`k`	the effective dimensionality of the final fit.
`k0`	(usually the same)
`x.ps`	the pseudo design matrix `X` (as returned by `qbsks2`).
`resid`	vector of residuals from the fit.
`fitted`	vector of fitted values from the fit.
`SSy`	the sum of squares around centered `y` (e.g. for computation of `R^2`.)
`lambda`	the penalty parameter used in the final fit.
`pp.lambda`	vector of all lambdas used for lambda search when `lambda` < 0 on input.
`pp.sic`	vector of Schwarz information criteria evaluated at `pp.lambda`; note that it is not quite sure how good these are for determining an optimal `lambda`.

References

Ng, P. and Maechler, M. (2007) A Fast and Efficient Implementation of Qualitatively Constrained Quantile Smoothing Splines, Statistical Modelling 7(4), 315-328.

Koenker, R. and Ng, P. (2005) Inequality Constrained Quantile Regression, Sankhya, The Indian Journal of Statistics 67, 418–440.

He, X. and Ng, P. (1999) COBS: Qualitatively Constrained Smoothing via Linear Programming; Computational Statistics 14, 315–337.

Koenker, R. and Ng, P. (1996) A Remark on Bartels and Conn's Linearly Constrained L1 Algorithm, ACM Transaction on Mathematical Software 22, 493–495.

Ng, P. (1996) An Algorithm for Quantile Smoothing Splines, Computational Statistics & Data Analysis 22, 99–118.

Bartels, R. and Conn A. (1980) Linearly Constrained Discrete L_1 Problems, ACM Transaction on Mathematical Software 6, 594–608.

A postscript version of the paper that describes the details of COBS can be downloaded from https://www2.nau.edu/PinNg/cobs.html.

Examples

x <- seq(-1,3,,150)
y <- (f.true <- pnorm(2*x)) + rnorm(150)/10
## specify pointwise constraints (boundary conditions)
con <- rbind(c( 1,min(x),0), # f(min(x)) >= 0
             c(-1,max(x),1), # f(max(x)) <= 1
             c(0,  0,   0.5))# f(0)      = 0.5

## obtain the median  REGRESSION  B-spline using automatically selected knots
Rbs <- cobs(x,y, constraint= "increase", pointwise = con)
Rbs
plot(Rbs, lwd = 2.5)
lines(spline(x, f.true), col = "gray40")
lines(predict(cobs(x,y)), col = "blue")
mtext("cobs(x,y)   # completely unconstrained", 3, col= "blue")

## compute the median  SMOOTHING  B-spline using automatically chosen lambda
Sbs <- cobs(x,y, constraint="increase", pointwise= con, lambda= -1)
summary(Sbs)
plot(Sbs) ## by default  includes  SIC ~ lambda

Sb1 <- cobs(x,y, constraint="increase", pointwise= con, lambda= -1,
            degree = 1)
summary(Sb1)
plot(Sb1)

plot(Sb1, which = 2) # only the  data + smooth
rug(Sb1$knots, col = 4, lwd = 1.6)# (too many knots)
xx <- seq(min(x) - .2, max(x)+ .2, len = 201)
pxx <- predict(Sb1, xx, interval = "both")
lines(pxx, col = 2)
mtext(" + pointwise and simultaneous 95% - confidence intervals")
matlines(pxx[,1], pxx[,-(1:2)], col= rep(c("green3","blue"), c(2,2)), lty=2)

[Package cobs version 1.3-8 Index]