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-rw-r--r--docs/reference/logLik.mkinfit.html8
1 files changed, 6 insertions, 2 deletions
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html
index 5fd6c6d7..b1901703 100644
--- a/docs/reference/logLik.mkinfit.html
+++ b/docs/reference/logLik.mkinfit.html
@@ -41,7 +41,9 @@ 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.
+ observation as standard deviation in calculating its likelihood
+ 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
estimated parameters is obtained by the number of degradation model
@@ -147,7 +149,9 @@ In the case of iterative reweighting, the variances obtained by this
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.</p>
+ observation as standard deviation in calculating its likelihood
+ 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
estimated parameters is obtained by the number of degradation model

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