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<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.
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"." />
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<h1>Calculated the log-likelihood of a fitted mkinfit object</h1>
<div class="hidden name"><code>logLik.mkinfit.Rd</code></div>
</div>
<div class="ref-description">
<p>This function simply calculates the product of the likelihood densities
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
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>
<pre class="usage"><span class='co'># S3 method for mkinfit</span>
<span class='fu'>logLik</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>object</th>
<td><p>An object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td>
</tr>
<tr>
<th>…</th>
<td><p>For compatibility with the generic method</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>An object of class <code>logLik</code> with the number of
estimated parameters (degradation model parameters plus variance
model parameters) as attribute.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='no'>sfo_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
<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'>c</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'>AIC</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
#> f_obs 6 205.8727
#> f_tc 6 143.8773
#> f_man 4 291.8000</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#arguments">Arguments</a></li>
<li><a href="#value">Value</a></li>
<li><a href="#examples">Examples</a></li>
</ul>
<h2>Author</h2>
Johannes Ranke
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