plot.khmal {quantreg}R Documentation

Plots Standardized and Khmaladzized Residual Processes

Description

The function makes 6 arrays of p plots based on the object of class "khmal" created by `rq.test.khmal' of quantile regression results. The 6 arrays are: (i) estimated coefficients; (ii) regression of slopes on the intercept; (iii) standardized residuals for the joint; (iv) standardized residuals for the coef by coef; (v) khmaladzized residuals for the joint, and (vi) khmaladzized residuals for the coef by coef hypothesis testing.

Usage

plot.khmal(x, nrow= ceiling(length(x$var.list)/2), ncol= 2, plotn = 1:6, 
        bcolor="gray", ... )

Arguments

x output of `rq.test.khmal'. plot.khmal() requires the output of `rq.test.khmal'.
var.list numerical list of variables to be plotted. By default all variables are plotted. A restricted set of variables can be specified by providing a numerical vector indicating the desired variables. The convention is that 1 corresponds to the intercept, 2 to the first independent variable entered in "formula" and so on. See example for further details.
nrow number of rows per page of plots. Automatically set by assuming that the number of columns is 2.
ncol number of plots per page of plots. Default 2.
plotn a numerical vector indicating which array of plots will be graphed. By default the 6 arrays described in `Description' are plotted. Useful to produce individual postscript files of each array. For example, specifying plotn = 1 in conjunction will postscript("01.ps") will yield an array of plots of the quantile regression estimated coefficients.
bcolor color of the confidence band by default "gray".
... other optional arguments passed to `plot'.

Value

Generates plots of object of class `khmal'. Please refer to "Description" for further details.

References

Koenker, Roger and Zhijie Xiao (2000), "Inference on the Quantile Regression Process'', unpublished. http://www.econ.uiuc.edu/~roger/research/inference/inference.html

Examples

data(barro)
fit.Lonly <- rq.test.khmal(y.net ~  lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
data = barro, location.scale = FALSE)
par(ask=interactive())
plot(fit.Lonly, var.list=c(2,4)) 

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