title: Plotting quantile regression sensitivity in ggplot
body: <p>I would like to ask for advice how to plot quantile standart errors like with basic function in <code>quantreg</code> package in R</p>
  
  <pre><code>library(quantreg)
  library(ggplot2)
  library(dplyr)
  
  QR.2 &lt;- rq(hp ~  disp + mpg + I(mpg^2) + qsec + am, data = mtcars, tau = 1:9/10)
  plot(summary(QR.2, se="boot"), ols=T)
  
  </code></pre>
  
  <p>Plots above shows quantile standart errors and confidence intervals. Is there a way how to reproduce same plot in ggplot?</p>
  
  <p>Trying code below is pretty good start however <code>geom_ribbon(aes(ymin=conf.low,ymax=conf.high),alpha=0.25, fill="#27408b")</code> does return "confidence intervals" however clearly those are not the same as produced in plot above.</p>
  
  <p>Is there a way how to get conf. intervals as in plot above?</p>
  
  <pre><code>rq(data=mtcars,
     tau= 1:9/10,
     formula = hp ~  disp + mpg + I(mpg^2) + qsec + am) %&gt;% 
    broom::tidy() %&gt;% 
    filter(!grepl("factor", term)) %&gt;%   
    filter(!grepl("Intercept", term)) %&gt;%   
    ggplot(aes(x=tau,y=estimate))+
    geom_point(color="#27408b", size = 3)+ 
    geom_line(color="#27408b", size = 1)+ 
    geom_smooth(method=  "lm", colour = "red", se = T)+  
    facet_wrap(~term, scales="free", ncol=2) + 
    geom_ribbon(aes(ymin=conf.low,ymax=conf.high),alpha=0.25, fill="#27408b")
  </code></pre>