| Rq {rms} | R Documentation |
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.
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)
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 |
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".
The author and developer of methodology in the quantreg package
is Roger Koenker.
Frank Harrell
## 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)