plot.khmal {quantreg} | R Documentation |
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.
plot.khmal(x, nrow= ceiling(length(x$var.list)/2), ncol= 2, plotn = 1:6, bcolor="gray", ... )
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'. |
Generates plots of object of class `khmal'. Please refer to "Description" for further details.
Koenker, Roger and Zhijie Xiao (2000), "Inference on the Quantile Regression Process'', unpublished. http://www.econ.uiuc.edu/~roger/research/inference/inference.html
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))