https://www.wiki.eigenvector.com/index.php?title=Lwr&feed=atom&action=historyLwr - Revision history2024-03-29T01:58:42ZRevision history for this page on the wikiMediaWiki 1.39.6https://www.wiki.eigenvector.com/index.php?title=Lwr&diff=11015&oldid=prevLyle at 22:10, 6 February 20202020-02-06T22:10:17Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwr % Launches an Analysis window with LWR as the selected method.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwr % Launches an Analysis window with LWR as the selected method.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Please note that the recommended way to build a LWR model from the command line is to use the Model Object. Please see [[EVRIModel_Objects | this wiki page on building models using the Model Object]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Please note that the recommended way to build <ins style="font-weight: bold; text-decoration: none;">and apply </ins>a LWR model from the command line is to use the Model Object. Please see [[EVRIModel_Objects | this wiki page on building <ins style="font-weight: bold; text-decoration: none;">and applying </ins>models using the Model Object]].</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Description===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Description===</div></td></tr>
</table>Lylehttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=10977&oldid=prevLyle at 22:16, 3 January 20202020-01-03T22:16:36Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:valid = lwr(x,y,model,''options''); %makes predictions with new X- & Y-block</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:valid = lwr(x,y,model,''options''); %makes predictions with new X- & Y-block</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwr % Launches an Analysis window with LWR as the selected method.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:lwr % Launches an Analysis window with LWR as the selected method.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Please note that the recommended way to build a LWR model from the command line is to use the Model Object. Please see [[EVRIModel_Objects | this wiki page on building models using the Model Object]].</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Description===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Description===</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm<ins style="font-weight: bold; text-decoration: none;">]], [[EVRIModel_Objects</ins>]]</div></td></tr>
</table>Lylehttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=10885&oldid=prevDonal: /* Description */2019-10-29T00:27:10Z<p><span dir="auto"><span class="autocomment">Description</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:27, 28 October 2019</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For more information on the basic LWR algorithm, see <tt>T. Naes, T. Isaksson, B. Kowalski, Anal Chem 62 (1990) 664-673.</tt></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For more information on the basic LWR algorithm, see <tt>T. Naes, T. Isaksson, B. Kowalski, Anal Chem 62 (1990) 664-673.</tt></div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>For details on the use of y distance when selecting nearest points (option <del style="font-weight: bold; text-decoration: none;">alphs</del>), see <tt>Z. Wang, T. Isaksson, B. R. Kowalski, (1994). Anal Chem 66 (1994) 249–260.</tt></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>For details on the use of y distance when selecting nearest points (option <ins style="font-weight: bold; text-decoration: none;">alpha</ins>), see <tt>Z. Wang, T. Isaksson, B. R. Kowalski, (1994). Anal Chem 66 (1994) 249–260.</tt></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note: Calling lwr with no inputs starts the graphical user interface (GUI) for this analysis method. There is a</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note: Calling lwr with no inputs starts the graphical user interface (GUI) for this analysis method. There is a</div></td></tr>
</table>Donalhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=10884&oldid=prevDonal: /* Description */2019-10-29T00:26:49Z<p><span dir="auto"><span class="autocomment">Description</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For more information on the basic LWR algorithm, see <tt>T. Naes, T. Isaksson, B. Kowalski, Anal Chem 62 (1990) 664-673.</tt></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For more information on the basic LWR algorithm, see <tt>T. Naes, T. Isaksson, B. Kowalski, Anal Chem 62 (1990) 664-673.</tt></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">For details on the use of y distance when selecting nearest points (option alphs), see <tt>Z. Wang, T. Isaksson, B. R. Kowalski, (1994). Anal Chem 66 (1994) 249–260.</tt></ins></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note: Calling lwr with no inputs starts the graphical user interface (GUI) for this analysis method. There is a</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note: Calling lwr with no inputs starts the graphical user interface (GUI) for this analysis method. There is a</div></td></tr>
</table>Donalhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=6004&oldid=previmported>Donal: /* Options */2019-05-05T06:32:07Z<p><span dir="auto"><span class="autocomment">Options</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the local regression model, if different from the number selected in the SSQ Table. The number of components selected in the SSQ table is used to generate the global PCA model which is used to select the local calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the local regression as were used in the global PCA model. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the local regression model, if different from the number selected in the SSQ Table. The number of components selected in the SSQ table is used to generate the global PCA model which is used to select the local calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the local regression as were used in the global PCA model. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0<del style="font-weight: bold; text-decoration: none;">.75</del>} ], <del style="font-weight: bold; text-decoration: none;">(1-alpha) </del>[0-1] Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0} ], <ins style="font-weight: bold; text-decoration: none;"> has value in range </ins>[0-1]<ins style="font-weight: bold; text-decoration: none;">. </ins>Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The default options can be retreived using: options = lwr('options');.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The default options can be retreived using: options = lwr('options');.</div></td></tr>
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</table>imported>Donalhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=6003&oldid=previmported>Benjamin: /* See Also */2018-01-11T18:55:18Z<p><span dir="auto"><span class="autocomment">See Also</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann<del style="font-weight: bold; text-decoration: none;">]], [[lwr</del>]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td></tr>
</table>imported>Benjaminhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=6002&oldid=previmported>Benjamin: /* See Also */2018-01-11T18:54:10Z<p><span dir="auto"><span class="autocomment">See Also</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[ann<ins style="font-weight: bold; text-decoration: none;">]], [[lwr</ins>]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td></tr>
</table>imported>Benjaminhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=6001&oldid=previmported>Benjamin: /* See Also */2018-01-11T18:51:56Z<p><span dir="auto"><span class="autocomment">See Also</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[analysis]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[analysis<ins style="font-weight: bold; text-decoration: none;">]], [[ann</ins>]], [[lwrpred]], [[modelstruct]], [[pls]], [[pcr]], [[preprocess]], [[svm]]</div></td></tr>
</table>imported>Benjaminhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=6000&oldid=previmported>Donal: /* Options */2017-08-14T17:15:25Z<p><span dir="auto"><span class="autocomment">Options</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''blockdetails''': [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''blockdetails''': [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*'''confidencelimit''': [ {'0.95'} ], confidence level for Q and T2 limits, a value of zero (0) disables calculation of confidence limits,</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*'''confidencelimit''': [ {'0.95'} ], confidence level for Q and T2 limits, a value of zero (0) disables calculation of confidence limits,</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the regression model, if different from the number selected in the SSQ Table <del style="font-weight: bold; text-decoration: none;">which </del>is used to generate the global PCA model used to select the local calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the local regression as were used in the global PCA model <del style="font-weight: bold; text-decoration: none;">used to select samples</del>. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the <ins style="font-weight: bold; text-decoration: none;">local </ins>regression model, if different from the number selected in the SSQ Table<ins style="font-weight: bold; text-decoration: none;">. The number of components selected in the SSQ table </ins>is used to generate the global PCA model <ins style="font-weight: bold; text-decoration: none;">which is </ins>used to select the local calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the local regression as were used in the global PCA model. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0.75} ], (1-alpha) [0-1] Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0.75} ], (1-alpha) [0-1] Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td></tr>
</table>imported>Donalhttps://www.wiki.eigenvector.com/index.php?title=Lwr&diff=5999&oldid=previmported>Donal: /* Options */2017-08-14T17:08:53Z<p><span dir="auto"><span class="autocomment">Options</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''blockdetails''': [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''blockdetails''': [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*'''confidencelimit''': [ {'0.95'} ], confidence level for Q and T2 limits, a value of zero (0) disables calculation of confidence limits,</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*'''confidencelimit''': [ {'0.95'} ], confidence level for Q and T2 limits, a value of zero (0) disables calculation of confidence limits,</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the regression model, if different from the number used to select calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the regression as were used to select samples. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* '''reglvs''': [] Used only when algorithm is 'pcr' or 'pls', this is the number of latent variables/principal components to use in the regression model, if different from the number <ins style="font-weight: bold; text-decoration: none;">selected in the SSQ Table which is used to generate the global PCA model </ins>used to select <ins style="font-weight: bold; text-decoration: none;">the local </ins>calibration samples. [] (Empty) implies LWRPRED should use the same number of latent variables in the <ins style="font-weight: bold; text-decoration: none;">local </ins>regression as were <ins style="font-weight: bold; text-decoration: none;">used in the global PCA model </ins>used to select samples. NOTE: This option is NOT used when algorithm is 'globalpcr'.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''iter''': [{5}] Iterations in determining local points. Used only when alpha > 0 (i.e. when using y-distance scaling).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0.75} ], (1-alpha) [0-1] Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* '''alpha''': [ {0.75} ], (1-alpha) [0-1] Weighting of y-distances in selection of local points. 0 = do not consider y-distances {default}, 1 = consider ONLY y-distances. With any positive alpha, the algorithm will tend to select samples which are close in both the PC space but which also have similar y-values. This is accomplished by repeating the prediction multiple times. In the first iteration, the selection of samples is done only on the PC space. Subsequent iterations take into account the comparison between predicted y-value of the new sample and the measured y-values of the calibration samples.</div></td></tr>
</table>imported>Donal