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)