# plot.iqr: Plot Quantile Regression Coefficients In qrcm: Quantile Regression Coefficients Modeling

## Description

Plots quantile regression coefficients β(p) as a function of p, based on a fitted model of class “`iqr`”.

## Usage

 ```1 2``` ```## S3 method for class 'iqr' plot(x, conf.int = TRUE, polygon = TRUE, which = NULL, ask = TRUE, ...) ```

## Arguments

 `x` an object of class “`iqr`”, typically the result of a call to `iqr`. `conf.int` logical. If TRUE, asymptotic 95% confidence intervals are added to the plot. `polygon` logical. If TRUE, confidence intervals are represented by shaded areas via `polygon`. Otherwise, dashed lines are used. `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. `...` additional graphical parameters, that can include xlim, ylim, xlab, ylab, col, lwd, cex.lab, cex.axis, axes, frame.plot. See `par`.

## Details

Using `iqr`, each quantile regression coefficient β(p) is described by a linear combination of known parametric functions of p. With this command, a plot of β(p) versus p is created. If ask = TRUE, an additional option permits plotting a Q-Q plot of the fitted cumulative distribution function (CDF), that should follow a U(0,1) distribution if the model is correctly specified. If the data are censored or truncated, this is assessed applying the Kaplan-Meier estimator to the fitted CDF values. See also `test.fit` for a formal test of uniformity.

## Author(s)

Paolo Frumento paolo.frumento@unipi.it

`iqr` for model fitting; `summary.iqr` and `predict.iqr` for model summary and prediction.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ``` # using simulated data n <- 1000 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) par(mfrow = c(1,2)) plot(iqr(y ~ x, formula.p = ~ slp(p,3)), ask = FALSE) # flexible fit with shifted Legendre polynomials ```

### Example output ```Loading required package: survival