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Diffstat (limited to 'docs/reference/logLik.mkinfit.html')
| -rw-r--r-- | docs/reference/logLik.mkinfit.html | 51 |
1 files changed, 11 insertions, 40 deletions
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index fc4193cb..0184d573 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -33,23 +33,12 @@ <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 - calculated using dnorm, i.e. assuming normal distribution. + calculated using dnorm, i.e. assuming normal distribution, + with of the mean predicted by the degradation model, and the + standard deviation predicted by the error model. 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 - 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 - 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"." /> + model parameters and the fitted error model parameters." /> <meta name="twitter:card" content="summary" /> @@ -80,7 +69,7 @@ In the case of iterative reweighting, the variances obtained by this </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.48.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.4</span> </span> </div> @@ -146,23 +135,12 @@ In the case of iterative reweighting, the variances obtained by this <div class="ref-description"> <p>This function simply calculates the product of the likelihood densities - calculated using <code><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>dnorm</a></code>, i.e. assuming normal distribution.</p> + calculated using <code><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>dnorm</a></code>, i.e. assuming normal distribution, + with of the mean predicted by the degradation model, and the + standard deviation predicted by the error model.</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><a href='https://www.rdocumentation.org/packages/stats/topics/sd'>sd</a></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 - 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 - 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> @@ -199,17 +177,10 @@ In the case of iterative reweighting, the variances obtained by this <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>) )</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> <span class='no'>d_t</span> <span class='kw'><-</span> <span class='no'>FOCUS_2006_D</span> - <span class='no'>d_t</span>[<span class='fl'>23</span>:<span class='fl'>24</span>, <span class='st'>"value"</span>] <span class='kw'><-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>NA</span>, <span class='fl'>NA</span>) <span class='co'># can't cope with zero values at the moment</span> - <span class='no'>f_nw</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span> - <span class='no'>f_obs</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='no'>f_tc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='no'>d_t</span>$<span class='no'>err</span> <span class='kw'><-</span> <span class='no'>d_t</span>$<span class='no'>value</span> <span class='co'># Manual weighting assuming sigma ~ y</span> - <span class='no'>f_man</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>, <span class='no'>f_man</span>)</div><div class='output co'>#> df AIC -#> f_nw 5 204.4619 + <span class='no'>f_nw</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span></div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='no'>f_obs</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='no'>f_tc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/AIC'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>)</div><div class='output co'>#> df AIC +#> f_nw 5 204.4486 #> f_obs 6 205.8727 -#> f_tc 6 143.8773 -#> f_man 4 291.8000</div><div class='input'> </div></pre> +#> f_tc 6 141.9656</div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> |