title: Error: The data used by step_impute_linear() did not have any rows where the imputation values were all complete
body: <p>I am using the recipe function and get an error when using the <code>step_impute_linear()</code> inside of the recipe function to impute NA's. Note that <code>step_impute_median</code> or <code>step_impute_mean</code> work without a problem. Also it does not matter if I use:</p>
  <ul>
  <li><code>step_impute_linear(all_predictors())</code> or,</li>
  <li><code>step_impute_linear(all_numeric(),.)</code> etc.</li>
  </ul>
  <p>None of the combinations work.</p>
  <p>Also not that other methods like:</p>
  <pre><code>step_impute_knn(all_nominal(),impute_with = all_predictors(),-has_role(&quot;ID&quot;))
  </code></pre>
  <p>fail too.</p>
  <p>I also checked the data and not all of the rows contain missing data also not all of the columns do.</p>
  <pre><code>dt_rec &lt;- recipe(
    OFFER_STATUS~ ., data = dt_training) %&gt;% 
    # 1. Define Role
    update_role(MO_ID, new_role = &quot;ID&quot;) %&gt;% 
    update_role(SO_ID, new_role = &quot;ID&quot;) %&gt;% 
    # turn dates into decimals
    step_mutate_at(where(is.Date), fn = decimal_date) %&gt;% 
    # impute all numeric columns with their median
    # 2. Impute
    # step_impute_median(all_numeric(),-has_role(&quot;ID&quot;))%&gt;%
    step_impute_linear(all_numeric(),impute_with = .,-has_role(&quot;ID&quot;))
    # ignoring novel factors
    # 3. Handle factor levels
    step_novel(all_predictors(), -all_numeric())  %&gt;%
    # impute all other nominal (character + factor) columns with the value &quot;none&quot;
    step_unknown(all_nominal(), new_level = &quot;none&quot;) %&gt;% 
    step_string2factor(all_nominal(), -all_outcomes(), -has_role(&quot;ID&quot;)) %&gt;% 
    # remove constant columns
    step_zv(all_predictors()) %&gt;% 
    # 4. Discretize
    # remove variables that have a high correlation with each other
    # as this will lead to multicollinearity
    step_corr(all_numeric(), threshold = 0.99) %&gt;% 
    # normalization --&gt; centering and scaling numeric variables
    # mean = 0 and Sd = 1
    step_normalize(all_numeric()) %&gt;%
    # 5. Dummy variables
    # creating dummary variables for nominal predictors
    step_dummy(all_nominal(), -all_outcomes())
  # 6. Normalization
  # 7. Multivariate transformation
  step_pca(all_numeric_predictors())
  
  dt_rec
  
  dt_rec %&gt;% summary()
  </code></pre>