plot.summary.rqs: 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 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

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.

Value

An array with all coefficients visualized (and associated confidence bands) is returned invisibly.

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.

Examples

Run this code

# NOT RUN {
## 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)
# }