Additive Quantile Regression Smoothing
Fitting function for additive quantile regression models with possible univariate and/or bivariate nonparametric terms estimated by total variation regularization.
rqss(formula, tau = 0.5, data = parent.frame(), weights, na.action, method = "sfn", lambda = NULL, contrasts = NULL, ztol = 1e-5, control, ...)
- a formula object, with the response on the left of a `~'
operator, and terms, separated by `+' operators, on the right.
The terms may include
qssterms that represent additive nonparametric components. These terms can be univariat
- the quantile to be estimated, this must be a number between 0 and 1,
- a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument.
- vector of observation weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the absolute residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative
- a function to filter missing data.
This is applied to the model.frame after any subset argument has been used.
The default (with
na.fail) is to create an error if any missing values are found. A possible alternative is
- the algorithmic method used to compute the fit. There are currently two options. Both are implementations of the Frisch--Newton interior point method described in detail in Portnoy and Koenker(1997). Both are implemented using sparse Chol
- can be either a scalar, in which case all the slope coefficients are assigned this value, or alternatively, the user can specify a vector of length equal to the number of linear covariates plus one (for the intercept) and these values will b
- a list giving contrasts for some or all of the factors
NULLappearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any ful
- A zero tolerance parameter used to determine the number of zero residuals in the fitted object which in turn determines the effective dimensionality of the fit.
- control argument for the fitting routines
- Other arguments passed to fitting routines
Total variation regularization for univariate and
bivariate nonparametric quantile smoothing is described
in Koenker, Ng and Portnoy (1994) and Koenker and Mizera(2003)
respectively. The additive model extension of this approach
depends crucially on the sparse linear algebra implementation
for R described in Koenker and Ng (2003). There are extractor
AIC that is
relevant to lambda selection. A more detailed description of
some recent developments of these methods is available from
within the package with
vignette("rqss"). Since this
function uses sparse versions of the interior point algorithm
it may also prove to be useful for fitting linear models
qss terms when the design has a sparse
structure, as for example when there is a complicated factor
- The function returns a fitted object representing the estimated
model specified in the formula. See
rqss.objectfor further details on this object, and references to methods to look at it.
 Koenker, R. and S. Portnoy (1997) The Gaussian Hare and the Laplacean Tortoise: Computability of Squared-error vs Absolute Error Estimators, (with discussion). Statistical Science 12, 279--300.
 Koenker, R., P. Ng and S. Portnoy, (1994) Quantile Smoothing Splines; Biometrika 81, 673--680.
 Koenker, R. and I. Mizera, (2003) Penalized Triograms: Total Variation Regularization for Bivariate Smoothing; JRSS(B) 66, 145--163.
 Koenker, R. and P. Ng (2003) SparseM: A Sparse Linear Algebra Package for R, J. Stat. Software.
n <- 200 x <- sort(rchisq(n,4)) z <- x + rnorm(n) y <- log(x)+ .1*(log(x))^2 + log(x)*rnorm(n)/4 + z plot(x, y-z) f.N <- rqss(y ~ qss(x, constraint= "N") + z) f.I <- rqss(y ~ qss(x, constraint= "I") + z) f.CI <- rqss(y ~ qss(x, constraint= "CI") + z) lines(x[-1], f.N $coef + f.N $coef[-(1:2)]) lines(x[-1], f.I $coef + f.I $coef[-(1:2)], col="blue") lines(x[-1], f.CI$coef + f.CI$coef[-(1:2)], col="red") ## A bivariate example data(CobarOre) fCO <- rqss(z ~ qss(cbind(x,y), lambda= .08), data=CobarOre) plot(fCO)