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| author | Johannes Ranke <jranke@uni-bremen.de> | 2018-11-26 09:49:58 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2018-11-26 09:49:58 +0100 |
| commit | 282f88f9e28e683f524d5e05d65d8b18ab856a8d (patch) | |
| tree | d4928410ba3f2d9d276e06e2149069d66aecb19c /docs/reference/logLik.mkinfit.html | |
| parent | 7aaa293d43989834be53d78298629aa3d23dc9a7 (diff) | |
Further improvement of the logLik.mkinfit help
Static documentation rebuilt by pkgdown
Diffstat (limited to 'docs/reference/logLik.mkinfit.html')
| -rw-r--r-- | docs/reference/logLik.mkinfit.html | 8 |
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 |