plot.piqr  R Documentation 
Produces a coefficient profile plot of the quantile
regression coefficient paths for a fitted model of
class “piqr
”.
## S3 method for class 'piqr'
plot(x, xvar=c("lambda", "objective", "grad", "beta"), pos.lambda,
label=FALSE, which=NULL, ask=TRUE, polygon=TRUE, ...)
x 
an object of class “ 
xvar 
What is on the Xaxis. "lambda" against the loglambda sequence, "objective" against the value
of the minimized integrated loss function and "grad" the loglambda sequence
against the gradient.
xvar = "beta" needs a lambda value to plot quantile regression coefficients

pos.lambda 
the position of a lambda in the sequence of the object of class “ 
label 
If TRUE, label the curves with variable sequence numbers. 
which 
an optional numerical vector indicating which coefficient(s) to plot. If which = NULL, all coefficients are plotted. 
ask 
logical. If which = NULL and ask = TRUE (the default), you will be asked interactively which coefficients to plot. 
polygon 
ogical. If TRUE, confidence intervals are represented by shaded areas via polygon. Otherwise, dashed lines are used. 
... 
additional graphical parameters, that can include xlim, ylim, xlab, ylab, col, lwd.
See 
A coefficient profile plot is produced.
Gianluca Sottile gianluca.sottile@unipa.ot
piqr
for model fitting; gof.piqr
for the model selection criteria; summary.piqr
and predict.piqr
for model summary and prediction.
# using simulated data
n < 300
x < runif(n)
qy < function(p,x){p^2 + x*log(p)}
# true quantile function: Q(p  x) = beta0(p) + beta1(p)*x, with
# beta0(p) = p^2
# beta1(p) = log(p)
y < qy(runif(n), x) # to generate y, plug uniform p in qy(p,x)
obj < piqr(y ~ x, formula.p = ~ slp(p,3), nlambda=50)
best < gof.piqr(obj, method="BIC", plot=FALSE)
par(mfrow = c(1,3))
plot(obj, xvar="lambda")
plot(obj, xvar="objective")
plot(obj, xvar="grad")
par(mfrow=c(1,2));plot(obj, xvar="beta", pos.lambda=best$posMinLambda, ask=FALSE)
# flexible fit with shifted Legendre polynomials
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