Rq {rms}R Documentation

rms Package Interface to quantreg Package

Description

The Rq function is the rms front-end to the quantreg package's rq function. print and latex methods are also provided, and a fitting function RqFit is defined for use in bootstrapping, etc. Its result is a function definition.

Usage

Rq(formula, tau = 0.5, data, subset, weights, na.action=na.delete,
   method = "br", model = FALSE, contrasts = NULL,
   se = "nid", hs = TRUE, x = FALSE, y = FALSE, ...)

## S3 method for class 'Rq':
print(x, digits=4, ...)

## S3 method for class 'Rq':
latex(object,
           file = paste(first.word(deparse(substitute(object))),
             ".tex", sep = ""), append=FALSE,
           which, varnames, columns=65, inline=FALSE, caption=NULL,
           ...)

RqFit(fit, wallow=TRUE, passdots=FALSE)

Arguments

formula model formula
tau the single quantile to estimate. Unlike rq you cannot estimate more than one quantile at one model fitting.
data
subset
weights
na.action
method
model
contrasts
se
hs see rq
x set to TRUE to store the design matrix with the fit. For print is an Rq object.
y set to TRUE to store the response vector with the fit
... other arguments passed to one of the rq fitting routines. For latex.Rq these are optional arguments passed to latexrms. Ignored for print.Rq.
digits number of significant digits used in formatting results in print.Rq.
object an object created by Rq
file
append
which
varnames
columns
inline
caption see latexrms
fit an object created by Rq
wallow set to TRUE if weights are allowed in the current context.
passdots set to TRUE if ... may be passed to the fitter

Value

Rq returns a list of class "rms", "lassorq" or "scadrq", "Rq", and "rq". RqFit returns a function definition. latex.Rq returns an object of class "latex".

Note

The author and developer of methodology in the quantreg package is Roger Koenker.

Author(s)

Frank Harrell

See Also

rq

Examples

## Not run: 
set.seed(1)
n <- 100
x1 <- rnorm(n)
y <- exp(x1 + rnorm(n)/4)
dd <- datadist(x1); options(datadist='dd')
fq2 <- Rq(y ~ pol(x1,2))
anova(fq2)
fq3 <- Rq(y ~ pol(x1,2), tau=.75)
anova(fq3)
pq2 <- Predict(fq2, x1=.)
pq3 <- Predict(fq3, x1=.)
p <- rbind(Median=pq2, Q3=pq3)
plot(p, ~ x1 | .set.)
# For superpositioning, with true curves superimposed
a <- function(x, y, ...) {
 x <- unique(x)
 col <- trellis.par.get('superpose.line')$col
 llines(x, exp(x), col=col[1], lty=2)
 llines(x, exp(x + qnorm(.75)/4), col=col[2], lty=2)
}
plot(p, addpanel=a)
## End(Not run)

[Package rms version 2.1-0 Index]