plot.piqr: Plot Penalized Quantile Regression Coefficients
In qrcmNP: Nonlinear and Penalized Quantile Regression Coefficients Modeling

Description Usage Arguments Details Author(s) See Also Examples

Produces a coefficient profile plot of the quantile regression coefficient paths for a fitted model of class “piqr”.

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## 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 “`piqr`”, typically the result of a call to `piqr`.
`xvar`	What is on the X-axis. "lambda" against the log-lambda sequence, "objective" against the value of the minimized integrated loss function and "grad" the log-lambda sequence against the gradient. xvar = "beta" needs a lambda value to plot quantile regression coefficients β(p \| θ(λ)) as a function of p, based on the fitted model of class “`piqr`”
`pos.lambda`	the position of a lambda in the sequence of the object of class “`piqr`”. Could be the best after selecting the result of a call to `gof.piqr`
`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 `par`.

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