R: Visualizing sequences of quantile regression summaries
plot.summary.rqs
R Documentation
Visualizing sequences of quantile regression summaries
Description
A sequence of coefficient estimates for quantile
regressions with varying tau parameters is visualized
along with associated confidence bands.
Usage
## 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 = "", ...)
Arguments
x
an object of class "summary.rqs" as produce by
applying the summary method to a rq object
(with a vector of tau values).
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
summary.rq, prior to calling
plot.summary.rqs. Note also that alpha = 1 - level.
ols
logical. Should a line for the OLS coefficient and their confidence
bands (as estimated by lm) be added?
mfrow, mar, ylim, main
graphical parameters. Suitable defaults are chosen
based on the coefficients to be visualized.
col
vector of color specification for rq coefficients
and the associated confidence polygon.
border
color specification for the confidence polygon. By default,
the second element of col is used.
lcol, lty
color and line type specification for OLS coefficients
and their confidence bounds.
cex, pch, type, xlab, ylab, ...
further graphical parameters
passed to points.
Details
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.
Value
An array with all coefficients visualized (and associated confidence bands)
is returned invisibly.
See Also
rq, plot.rqs
Examples
## 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)