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Diffstat (limited to 'docs/reference/logLik.mkinfit.html')
| -rw-r--r-- | docs/reference/logLik.mkinfit.html | 19 |
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'><-</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> |