quantreg: plot.rqss – R documentation

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plot.rqss

Plot Method for rqss Objects

Description

Takes a fitted rqss object produced by rqss() and plots the component smooth functions that make up the ANOVA decomposition. Since the components "omit the intercept" the estimated intercept is added back in – this facilitates the comparison of quantile fits particularly. For models with a partial linear component or several qss components it may be preferable to plot the output of predict.rqss. Note that these functions are intended to plot rqss objects only, attempting to plot summary.rqss objects just generates a warning message.

Usage

## S3 method for class 'rqss'
plot(x, rug = TRUE, jit = TRUE, bands = NULL, coverage = 0.95,
	add = FALSE, shade = TRUE, select = NULL, pages = 0, titles = NULL, 
	bcol = NULL, ...)
## S3 method for class 'qss1'
plot(x, rug = TRUE, jit = TRUE, add = FALSE, ...)
## S3 method for class 'qts1'
plot(x, rug = TRUE, jit = TRUE, add = FALSE, ...)
## S3 method for class 'qss2'
plot(x, render = "contour", ncol = 100, zcol = NULL, ...)
## S3 method for class 'summary.rqss'
plot(x, ...)

Arguments

`x`	a fitted `rqss` object produced by `rqss()`.
`...`	additional arguments for the plotting algorithm
`rug`	if TRUE, a rugplot for the x-coordinate is plotted
`jit`	if TRUE, the x-values of the rug plot are jittered
`bands`	if TRUE, confidence bands for the smoothed effects are plotted, if "uniform" then uniform bands are plotted, if "both" then both the uniform and the pointwise bands are plotted.
`coverage`	desired coverage probability of confidence bands, if requested
`select`	vector of indices of qss objects to be plotted, by default all
`pages`	number of pages desired for the plots
`render`	a character specifying the rendering for bivariate fits; either `"contour"` (default) or `"rgl"`. The latter requires package rgl.
`add`	if TRUE then add qss curve to existing (usually) scatterplot, otherwise initiate a new plot
`shade`	if TRUE then shade the confidence band
`titles`	title(s) as vector of character strings, by default titles are chosen for each plot as "Effect of CovariateName"
`bcol`	vector of two colors for confidence bands
`ncol, zcol`	Only for `render = "rgl"`: number of colors and z values for color construction.

Details

For univariate qss components with Dorder = 0 the fitted function is piecewise constant, not piecewise linear. In this case the constraints are limited to increasing, decreasing or none. If bands == "uniform" then the bands are uniform bands based on the Hotelling (1939) tube approach. See also Naiman (1986), Johansen and Johnstone (1990), Sun and Loader (1994), and Krivobokova, Kneib, and Claeskens (2009), in particular the computation of the "tube length" is based on the last of these references. If bands is non null, and not "uniform" then pointwise bands are returned. Since bands for bivariate components are not (yet) supported, if requested such components will be returned as NULL.

Value

The function produces plots for the ANOVA components as a side effect. For "qss1" the "add = TRUE" can be used to overplot the fit on a scatterplot. When there are multiple pages required "par(ask = TRUE)" is turned on so that the plots may be examined sequentially. If bands != NULL then a list with three components for each qss component is returned (invisibly):

`x`	The x coordinates of the confidence bands
`blo`	The y coordinates of the lower confidence curve, if `bands = "both"` then this is a matrix with two columns
`bhi`	The y coordinates of the upper confidence curve, if `bands = "both"` then this is a matrix with two columns

Author(s)

Roger Koenker

References

[1] Hotelling, H. (1939): “Tubes and Spheres in $n$-spaces, and a class of statistical problems,” Am J. Math, 61, 440–460.

[2] Johansen, S., and I.M. Johnstone (1990): “Hotelling's Theorem on the Volume of Tubes: Some Illustrations in Simultaneous Inference and Data Analysis,” The Annals of Statistics, 18, 652–684.

[3] Naiman, D. (1986) Conservative confidence bands in curvilinear regression, The Annals of Statistics, 14, 896–906.

[4] Sun, J. and C.R. Loader, (1994) Simultaneous confidence bands for linear regression and smoothing, The Annals of Statistics, 22, 1328–1345.

[5] Krivobokova, T., T. Kneib, and G. Claeskens (2009) Simultaneous Confidence Bands for Penalized Spline Estimators, preprint.

[6] Koenker, R. (2010) Additive Models for Quantile Regression: Model Selection and Confidence Bandaids, preprint.

Examples

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)
fN <- rqss(y~qss(x,constraint="N")+z)
plot(fN)
fI <- rqss(y~qss(x,constraint="I")+z)
plot(fI,  col="blue")
fCI <- rqss(y~qss(x,constraint="CI")+z)
plot(fCI, col="red")


## A bivariate example
data(CobarOre)
fCO <- rqss(z~qss(cbind(x,y),lambda=.08), data = CobarOre)
plot(fCO)

quantreg

Quantile Regression

v5.85

GPL (>= 2)

Authors

Roger Koenker [cre, aut], Stephen Portnoy [ctb] (Contributions to Censored QR code), Pin Tian Ng [ctb] (Contributions to Sparse QR code), Blaise Melly [ctb] (Contributions to preprocessing code), Achim Zeileis [ctb] (Contributions to dynrq code essentially identical to his dynlm code), Philip Grosjean [ctb] (Contributions to nlrq code), Cleve Moler [ctb] (author of several linpack routines), Yousef Saad [ctb] (author of sparskit2), Victor Chernozhukov [ctb] (contributions to extreme value inference code), Ivan Fernandez-Val [ctb] (contributions to extreme value inference code), Brian D Ripley [trl, ctb] (Initial (2001) R port from S (to my everlasting shame -- how could I have been so slow to adopt R!) and for numerous other suggestions and useful advice)

Initial release