title: Find quantiles of gamma like distribution
body: <p>The data is gamma like distributed. </p>
  
  <p><img src="https://i.stack.imgur.com/TdIWF.jpg" alt="enter image description here"></p>
  
  <p>To replicate the data would be something like this:</p>
  
  <p>a) first find the distrib. parameters of the true data:</p>
  
  <pre><code>fitdist(datag, "gamma", optim.method="Nelder-Mead")
  </code></pre>
  
  <p>b) Use the parameters shape, rate, scale to simulate data:</p>
  
  <pre><code>data &lt;- rgamma(10000, shape=0.6, rate=4.8, scale=1/4.8)
  </code></pre>
  
  <p>To find quantiles using the qgamma function in r, would be just:</p>
  
  <p>EDIT: </p>
  
  <pre><code>qgamma(c(seq(1,0.1,by=-0.1)), shape=0.6, rate =4.8, scale = 1/4.8, log = FALSE)
  </code></pre>
  
  <p>How I can find quantiles for my true data (not simulated with rgamma)? </p>
  
  <p>Please note that the quantile r function returns the desired quantiles of the true data (datag) but these are as I understand assuming the data are normally distributed. As you can see they are clearly not.</p>
  
  <pre><code>quantile(datag, seq(0,1, by=0.1), type=7)
  </code></pre>
  
  <p>What function in r to use or otherwise how to obtain statistically the quantiles for the highly skewed data? </p>
  
  <p>In addition, would this make sense somewhat? But still not getting the lower values!</p>
  
  <pre><code>Fn &lt;- ecdf(datag)
  Fn(seq(0.1,1,by=0.1))
  </code></pre>