title: Access general confidence boundaries of cronbach's alpha in R psych
body: <p>I have a dataframe on which I use psych::alpha. In the output there are general confidence boundaries around a general cronbach's alpha value. I want to access those but they don't appear in the results when I save the output as a variable. In the <a href="https://www.rdocumentation.org/packages/psych/versions/2.1.3/topics/alpha" rel="nofollow noreferrer">documentation</a> they're called itemboot.ci but that doesn't exist in the alpha object.enter code here</p>
  <pre><code>library(psych)
  set.seed(1)
  foo &lt;- data.frame(X = rnorm(25), Y = rnorm(25), Z = rnorm(25))
  bar &lt;- alpha(foo)
  </code></pre>
  <p><code>alpha(foo)</code> and <code>bar</code> both give this output:</p>
  <pre><code>Reliability analysis   
  Call: alpha(x = foo)
  
    raw_alpha std.alpha G6(smc) average_r      S/N  ase mean   sd median_r
       0.023  -0.00091  0.0049    -3e-04 -0.00091 0.32 0.12 0.54    0.022
  
   lower alpha upper     95% confidence boundaries
  -0.61 0.02 0.66 
  
   Reliability if an item is dropped:
    raw_alpha std.alpha G6(smc) average_r    S/N alpha se var.r  med.r
  X     0.039     0.043   0.022     0.022  0.045     0.35    NA  0.022
  Y     0.093     0.094   0.049     0.049  0.103     0.36    NA  0.049
  Z    -0.148    -0.155  -0.072    -0.072 -0.134     0.44    NA -0.072
  
   Item statistics 
     n raw.r std.r r.cor  r.drop  mean   sd
  X 25  0.58  0.56 -0.12  0.0027 0.169 0.95
  Y 25  0.41  0.55 -0.36 -0.0296 0.032 0.71
  Z 25  0.72  0.62  0.61  0.0543 0.165 1.11
  </code></pre>
  <p>Where</p>
  <pre><code>lower alpha upper     95% confidence boundaries
  -0.61 0.02 0.66
  </code></pre>
  <p>are the values I want to access and save as a variable.
  <code>bar$itemboot.ci</code>
  gives NULL even though it's mentioned in the <a href="https://www.rdocumentation.org/packages/psych/versions/2.1.3/topics/alpha" rel="nofollow noreferrer">documentation</a>
  and even looping through a doesn't show the confidence boundaries:</p>
  <pre><code>for(i in 1:length(bar)){
      print(bar[i])
  }
  </code></pre>
  <p>gives:</p>
  <pre><code>$total
    raw_alpha    std.alpha     G6(smc)    average_r           S/N      ase      mean       sd   median_r
   0.02297052 -0.000914273 0.004926574 -0.000304572 -0.0009134379 0.323334 0.1221019 0.544037 0.02180445
  
  $alpha.drop
      raw_alpha   std.alpha     G6(smc)   average_r         S/N  alpha se var.r       med.r
  X  0.03880703  0.04267832  0.02180445  0.02180445  0.04458096 0.3488970    NA  0.02180445
  Y  0.09264777  0.09365915  0.04913033  0.04913033  0.10333767 0.3588215    NA  0.04913033
  Z -0.14776806 -0.15482062 -0.07184849 -0.07184849 -0.13406464 0.4395377    NA -0.07184849
  
  $item.stats
     n     raw.r     std.r      r.cor       r.drop       mean        sd
  X 25 0.5843116 0.5644059 -0.1232943  0.002682803 0.16866521 0.9501080
  Y 25 0.4056801 0.5486244 -0.3639981 -0.029593160 0.03223135 0.7062806
  Z 25 0.7151930 0.6184929  0.6088273  0.054340331 0.16540907 1.1051952
  
  $response.freq
  NULL
  
  $keys
  X Y Z 
  1 1 1 
  
  $scores
   [1] -0.094825557 -0.194726192 -0.655087102 -0.004077450  0.726824345  0.839537022  0.005806616  0.027287230  0.363898646
  [10] -0.605834183  1.166134789 -0.014562240  0.003061795 -0.362224119  0.381611153 -0.006888302 -0.691503524  1.035451483
  [19]  0.510379244  0.692585766  0.228997248  0.145590611  0.484608087 -1.011931847  0.082433358
  
  $nvar
  [1] 3
  
  $boot.ci
  NULL
  
  $boot
  NULL
  
  $Unidim
  $Unidim$Unidim
  [1] -0.0006057996
  
  
  $var.r
  [1] 0.004025575
  
  $Fit
  $Fit$Fit.off
  [1] 0.9973251
  
  
  $call
  alpha(x = foo)
  
  $title
  NULL
  </code></pre>