title: Calculate 95 % credible interval for multiple columns in data frame R
body: <p>I need to get/calculate the 95 % credible interval for my data. My data consists of ten columns and over 5000 rows. Here is some example data.</p>
  <pre><code>data &lt;- data.frame(A = c(-7.595932, -6.451768, -4.682111, -8.781488, -4.251690), 
                     B = c(0.8324450, 0.9451657, 0.8773759, 0.6044753, 0.6553995),
                     C = c(22.747480, 15.477470, 18.745407, 9.622865, 21.137619), 
                     D = c(-11.684762, -13.474299, -9.783277, -7.747501, -12.352081))
  </code></pre>
  <p>I am just not sure which function to use since I get different results each time and it only works with one column at a time. I have tried the following functions:</p>
  <pre><code>ci(data$`A`, confidence = 0.95)  ## R package gmodels
  </code></pre>
  <p>and</p>
  <pre><code>CI(data$`A`, confidence = 0.95) ##R package Rmisc
  </code></pre>
  <p>Have anyone else experienced the same problem?</p>