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diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index ef62212a..d95e1c28 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -36,13 +36,8 @@ <meta property="og:title" content="Two component error model — sigma_twocomp" /> -<meta property="og:description" content="Function describing the standard deviation of the measurement error - in dependence of the measured value \(y\): -$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ - sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2) -This is the error model used for example by Werner et al. (1978). The model - proposed by Rocke and Lorenzato (1995) can be written in this form as well, - but assumes approximate lognormal distribution of errors for high values of y." /> +<meta property="og:description" content="Function describing the standard deviation of the measurement error in +dependence of the measured value \(y\):" /> <meta name="twitter:card" content="summary" /> @@ -139,13 +134,8 @@ This is the error model used for example by Werner et al. (1978). The model </div> <div class="ref-description"> - <p>Function describing the standard deviation of the measurement error - in dependence of the measured value \(y\):</p> -<p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ - sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)</p> -<p>This is the error model used for example by Werner et al. (1978). The model - proposed by Rocke and Lorenzato (1995) can be written in this form as well, - but assumes approximate lognormal distribution of errors for high values of y.</p> + <p>Function describing the standard deviation of the measurement error in +dependence of the measured value \(y\):</p> </div> <pre class="usage"><span class='fu'>sigma_twocomp</span>(<span class='no'>y</span>, <span class='no'>sigma_low</span>, <span class='no'>rsd_high</span>)</pre> @@ -159,18 +149,27 @@ This is the error model used for example by Werner et al. (1978). The model </tr> <tr> <th>sigma_low</th> - <td><p>The asymptotic minimum of the standard deviation for low observed values</p></td> + <td><p>The asymptotic minimum of the standard deviation for low +observed values</p></td> </tr> <tr> <th>rsd_high</th> - <td><p>The coefficient describing the increase of the standard deviation with - the magnitude of the observed value</p></td> + <td><p>The coefficient describing the increase of the standard +deviation with the magnitude of the observed value</p></td> </tr> </table> <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> <p>The standard deviation of the response variable.</p> + <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> + + <p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = +sqrt(sigma_low^2 + y^2 * rsd_high^2)</p> +<p>This is the error model used for example by Werner et al. (1978). The model +proposed by Rocke and Lorenzato (1995) can be written in this form as well, +but assumes approximate lognormal distribution of errors for high values of +y.</p> <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> <p>Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978) @@ -185,6 +184,7 @@ This is the error model used for example by Werner et al. (1978). The model <ul class="nav nav-pills nav-stacked"> <li><a href="#arguments">Arguments</a></li> <li><a href="#value">Value</a></li> + <li><a href="#details">Details</a></li> <li><a href="#references">References</a></li> </ul> |