Add AIC method for mmkin column objects

Static documentation rebuilt by pkgdown

1 files changed, 13 insertions, 6 deletions

diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html
index b1901703..33d9eb36 100644
--- a/docs/reference/logLik.mkinfit.html
+++ b/docs/reference/logLik.mkinfit.html
@@ -34,15 +34,15 @@
 
 <meta property="og:description" content="This function simply calculates the product of the likelihood densities
   calculated using dnorm, i.e. assuming normal distribution.
-The total number of estimated parameters returned with the value 
-  of the likelihood is calculated as the sum of fitted degradation 
+The total number of estimated parameters returned with the value
+  of the likelihood is calculated as the sum of fitted degradation
   model parameters and the fitted error model parameters.
 For the case of unweighted least squares fitting, we calculate one
   constant standard deviation from the residuals using sd
   and add one to the number of fitted degradation model parameters.
 For the case of manual weighting, we use the weight given for each
   observation as standard deviation in calculating its likelihood
-  and the total number of estimated parameters is equal to the 
+  and the total number of estimated parameters is equal to the
   number of fitted degradation model parameters.
 In the case of iterative reweighting, the variances obtained by this
   procedure are used in the likelihood calculations, and the number of
@@ -142,15 +142,15 @@ In the case of iterative reweighting, the variances obtained by this
     
     <p>This function simply calculates the product of the likelihood densities
   calculated using <code>dnorm</code>, i.e. assuming normal distribution.</p>
-<p>The total number of estimated parameters returned with the value 
-  of the likelihood is calculated as the sum of fitted degradation 
+<p>The total number of estimated parameters returned with the value
+  of the likelihood is calculated as the sum of fitted degradation
   model parameters and the fitted error model parameters.</p>
 <p>For the case of unweighted least squares fitting, we calculate one
   constant standard deviation from the residuals using <code>sd</code>
   and add one to the number of fitted degradation model parameters.</p>
 <p>For the case of manual weighting, we use the weight given for each
   observation as standard deviation in calculating its likelihood
-  and the total number of estimated parameters is equal to the 
+  and the total number of estimated parameters is equal to the
   number of fitted degradation model parameters.</p>
 <p>In the case of iterative reweighting, the variances obtained by this
   procedure are used in the likelihood calculations, and the number of
@@ -183,6 +183,11 @@ In the case of iterative reweighting, the variances obtained by this
   estimated parameters (degradation model parameters plus variance
   model parameters) as attribute.</p>
     
+    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+    <div class='dont-index'><p>Compare the AIC of columns of <code><a href='mmkin.html'>mmkin</a></code> objects using
+  <code><a href='AIC.mmkin.html'>AIC.mmkin</a></code>.</p></div>
+    
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>  <span class='no'>sfo_sfo</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
@@ -207,6 +212,8 @@ In the case of iterative reweighting, the variances obtained by this
       <li><a href="#arguments">Arguments</a></li>
       
       <li><a href="#value">Value</a></li>
+
+      <li><a href="#see-also">See also</a></li>
       
       <li><a href="#examples">Examples</a></li>
     </ul>