title: Predict values from model stored in a column
body: <p>I have a large dataset (subset below) where I need to fit models to each subset of data (grouped by several factors: Round, Level, Temp), and then plot both the model fits and confidence intervals. The predict function works fine when the model is stored to a variable, rather than to a column.</p>
  <p>I can nest the data based on the factors and fit each model, which is then stored to a column. But I can't quite figure out how to then take that model (stored in the column) and call it in the predict function.</p>
  <p>This code throws:</p>
  <blockquote>
  <p>Error: Problem with <code>mutate()</code> column <code>predicted</code>. i <code>predicted = predict(model, data.frame(dose = Day))</code>. x no applicable method for 'predict' applied to an object of class &quot;list&quot; Run <code>rlang::last_error()</code> to see</p>
  </blockquote>
  <pre><code>library(tidyverse)
  library(drc)
  
  b&lt;- structure(list(Round = c(&quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, 
  &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1a&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, 
  &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;1b&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, 
  &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;, &quot;4&quot;), 
      Temp...3 = c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10), Level = structure(c(1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
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      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 
      2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
      3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
      3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 
      1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c(&quot;low&quot;, 
      &quot;med&quot;, &quot;high&quot;), class = &quot;factor&quot;), PerMort = c(0, 0, 0, 0, 
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      0, 0, 0, 0.04, 0, 0, 0, 0.02, 0, 0, 0, 0, 0, 0, 0, 0.04, 
      0, 0, 0, 0.02, 0, 0, 0, 0, 0.0344827586206897, 0, 0, 0.04, 
      0, 0, 0, 0.02, 0, 0, 0.0196078431372549, 0.0169491525423729, 
      0.0862068965517241, 0.0526315789473684, 0, 0.04, 0, 0, 0.0769230769230769, 
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      0, 0.04, 0, 0.0196078431372549, 0.115384615384615, 0.04, 
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      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0287769784172662, 0.0227272727272727, 
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      0.539130434782609, 0.80672268907563, 0.976923076923077, 0.972413793103448, 
      0.990740740740741, 1, 0.0303030303030303, 0.0863309352517986, 
      0.0833333333333333, 0.0434782608695652, 0.954128440366973, 
      0.854545454545454, 0.756521739130435, 0.882352941176471, 
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      NA, NA, NA, NA, 1, 1, 0.990740740740741, 1, 0.0303030303030303, 
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      0.981651376146789, 0.945454545454545, 0.878260869565217, 
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      0.990825688073395, 0.972727272727273, 0.947826086956522, 
      0.991596638655462, 1, 1, 1, 1, 0.0505050505050505, 0.100719424460432, 
      0.0984848484848485, 0.0434782608695652, 0.990825688073395, 
      1, 0.982608695652174, 0.991596638655462, 1, 1, 1, 1, 0.0707070707070707, 
      0.129496402877698, 0.106060606060606, 0.0695652173913043, 
      1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
      0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0.0126582278481013, 
      0, 0.0125, 0.0125, 0.0101010101010101, 0.0212765957446809, 
      0, 0, 0.0104166666666667, 0, 0, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, 0.0526315789473684, 0.0632911392405063, 
      0.0617283950617284, 0.0625, 0.0625, 0.0505050505050505, 0.0425531914893617, 
      0.0104166666666667, 0.0315789473684211, 0.0104166666666667, 
      0.0104166666666667, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, 0.0789473684210526, 0.151898734177215, 0.135802469135802, 
      0.1, 0.0875, 0.101010101010101, 0.0851063829787234, 0.0729166666666667, 
      0.242105263157895, 0.177083333333333, 0.0729166666666667, 
      0.122448979591837, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, 0.0921052631578947, 0.151898734177215, 0.148148148148148, 
      0.1, 0.1125, 0.171717171717172, 0.138297872340426, 0.09375, 
      0.505263157894737, 0.395833333333333, 0.239583333333333, 
      0.408163265306122, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, 0.0921052631578947, 0.151898734177215, 0.148148148148148, 
      0.1, 0.275, 0.292929292929293, 0.223404255319149, 0.135416666666667, 
      0.831578947368421, 0.666666666666667, 0.5625, 0.76530612244898, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.118421052631579, 0.164556962025316, 
      0.172839506172839, 0.1, 0.3875, 0.343434343434343, 0.319148936170213, 
      0.21875, 0.905263157894737, 0.802083333333333, 0.729166666666667, 
      0.897959183673469, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, 0.144736842105263, 0.177215189873418, 0.185185185185185, 
      0.1, 0.55, 0.424242424242424, 0.436170212765957, 0.270833333333333, 
      0.957894736842105, 0.885416666666667, 0.802083333333333, 
      0.928571428571429, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, 0.25, 0.177215189873418, 0.197530864197531, 0.1, 
      0.6625, 0.565656565656566, 0.563829787234043, 0.34375, 0.968421052631579, 
      0.958333333333333, 0.875, 0.979591836734694), Day = c(0, 
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 
      3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 9, 9, 9, 
      9, 9, 9, 9, 9, 9, 9, 9, 9, 12, 12, 12, 12, 12, 12, 12, 12, 
      12, 12, 12, 12, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 
      15, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 21, 21, 
      21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 24, 24, 24, 
      24, 24, 24, 24, 24, 24, 24, 27, 27, 27, 27, 27, 27, 27, 27, 
      27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 
      3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
      6, 6, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 12, 12, 12, 
      12, 12, 12, 12, 12, 12, 12, 12, 12, 15, 15, 15, 15, 15, 15, 
      15, 15, 15, 15, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
      17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
      21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 24, 
      24, 24, 24, 24, 24, 24, 24, 24, 24, 27, 27, 27, 27, 27, 27, 
      27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
      0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 
      2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 
      4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 
      6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 
      9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 
      10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 
      12, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 16, 16, 
      16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 
      17, 17, 17, 17, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 
      19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 
      20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 
      24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 26, 26, 26, 26, 26, 
      26, 26, 26, 26, 26, 26, 26, 28, 28, 28, 28, 28, 28, 28, 28, 
      28, 28, 28, 28)), row.names = c(NA, -480L), class = c(&quot;tbl_df&quot;, 
  &quot;tbl&quot;, &quot;data.frame&quot;))
  
  
  
  c&lt;- b %&gt;% filter(!is.na(Round)) %&gt;% 
    dplyr::select(Round, Temp...3, Level, PerMort, Day) %&gt;% 
    nest(dataset = c(-Round, -Temp...3, -Level) ) %&gt;% 
    mutate(model = map(dataset, ~drc::drm(formula = .x$PerMort~.x$Day, fct = LL.3()) )) %&gt;% 
    unnest(dataset) %&gt;% 
    mutate(predicted = predict(model, data.frame(dose = Day )),
           confidencedown = predict(model, data.frame(dose = Day ), interval =&quot;confidence&quot;, level = 0.95)[,2],
           confidenceup = predict(model, data.frame(dose = Day ), interval =&quot;confidence&quot;, level = 0.95)[,3])
  </code></pre>