Visualizing sequences of quantile regression summaries
A sequence of coefficient estimates for quantile
regressions with varying tau
parameters is visualized
along with associated confidence bands.
## S3 method for class 'summary.rqs' plot(x, parm = NULL, level = 0.9, ols = TRUE, mfrow = NULL, mar = NULL, ylim = NULL, main = NULL, col = gray(c(0, 0.75)), border = NULL, lcol = 2, lty = 1:2, cex = 0.5, pch = 20, type = "b", xlab = "", ylab = "", ...)
x |
an object of class |
parm |
a specification of which parameters are to be plotted, either a vector of numbers or a vector of names. By default, all parameters are considered. |
level |
Confidence level of bands. When using
the rank based confidence intervals in summary, which is the default
method for sample sizes under 1000, you will need to control the level
of the intervals by passing the parameter alpha to
|
ols |
logical. Should a line for the OLS coefficient and their confidence
bands (as estimated by |
mfrow, mar, ylim, main |
graphical parameters. Suitable defaults are chosen
based on the coefficients to be visualized. It can be useful to use a common
vertical scale when plotting as a way of comparing confidence bands constructed
by different methods. For this purpose one can specify a |
col |
vector of color specification for |
border |
color specification for the confidence polygon. By default,
the second element of |
lcol, lty |
color and line type specification for OLS coefficients and their confidence bounds. |
cex, pch, type, xlab, ylab, ... |
further graphical parameters
passed to |
The plot
method for "summary.rqs"
objects visualizes
the coefficients along with their confidence bands. The bands can be
omitted by using the plot
method for "rqs"
objects directly.
A list with components z
, an array with all coefficients visualized
(and associated confidence bands), and Ylim
, a 2 by p matrix containing
the y plotting limits. The latter component may be useful for establishing a
common scale for two or more similar plots. The list is returned invisibly.
## fit Engel models (in levels) for tau = 0.1, ..., 0.9 data("engel") fm <- rq(foodexp ~ income, data = engel, tau = 1:9/10) sfm <- summary(fm) ## visualizations plot(sfm) plot(sfm, parm = 2, mar = c(5.1, 4.1, 2.1, 2.1), main = "", xlab = "tau", ylab = "income coefficient", cex = 1, pch = 19)
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