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Diffstat (limited to 'docs/reference/logLik.mkinfit.html')
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1 files changed, 30 insertions, 2 deletions
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 250bc1d8..5fd6c6d7 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -33,7 +33,21 @@ <meta property="og:title" content="Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit" /> <meta property="og:description" content="This function simply calculates the product of the likelihood densities - calc" /> + 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 + 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. +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 + parameters plus the number of variance model parameters, i.e. the number of + observed variables if the reweighting method is "obs", and two if the + reweighting method is "tc"." /> <meta name="twitter:card" content="summary" /> @@ -125,7 +139,21 @@ <div class="ref-description"> <p>This function simply calculates the product of the likelihood densities - calc</p> + 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 + 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.</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 + parameters plus the number of variance model parameters, i.e. the number of + observed variables if the reweighting method is "obs", and two if the + reweighting method is "tc".</p> </div> |