From 282f88f9e28e683f524d5e05d65d8b18ab856a8d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 26 Nov 2018 09:49:58 +0100 Subject: Further improvement of the logLik.mkinfit help Static documentation rebuilt by pkgdown --- docs/reference/logLik.mkinfit.html | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'docs/reference/logLik.mkinfit.html') 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 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 -- cgit v1.2.1