https://wiki.statsape.com/index.php?action=history&feed=atom&title=PLS%E5%9B%9E%E5%BD%92 2026-05-15T16:06:29Z 本wiki上该页面的版本历史 MediaWiki 1.39.6 https://wiki.statsape.com/index.php?title=PLS%E5%9B%9E%E5%BD%92&diff=6072&oldid=prev 2024-01-19T11:08:37Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="zh-Hans-CN"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年1月19日 (五) 19:08的版本</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l7">第7行：</td> <td colspan="2" class="diff-lineno">第7行：</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>|productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用</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>|productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用</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>|productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署</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>|productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署</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>|nodeenglishname=<del style="font-weight: bold; text-decoration: none;">[[Has english name::</del>PLS Regression<del style="font-weight: bold; text-decoration: none;">]]</del></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>|nodeenglishname=PLS Regression</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>|abbreviation=<del style="font-weight: bold; text-decoration: none;">[[Has abbreviation::</del>PLSReg<del style="font-weight: bold; text-decoration: none;">]]</del></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>|abbreviation=PLSReg</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>|funcmaincategory=数据分析</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>|funcmaincategory=数据分析</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>|funcsubcategory=[[DataAGM Lv1 Cat::回归分析]]</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>|funcsubcategory=[[DataAGM Lv1 Cat::回归分析]]</div></td></tr> <tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l25">第25行：</td> <td colspan="2" class="diff-lineno">第25行：</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>|statsapewikiurl=https://wiki.statsape.com/PLS回归</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>|statsapewikiurl=https://wiki.statsape.com/PLS回归</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>|previousnode=[[多项式回归]]</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>|previousnode=[[多项式回归]]</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>|nextnode=[[<del style="font-weight: bold; text-decoration: none;">广义线性回归_逆概率加权联合标准差</del>]]</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; 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Zeroclanzhang https://wiki.statsape.com/index.php?title=PLS%E5%9B%9E%E5%BD%92&diff=5314&oldid=prev 2024-01-18T14:11:18Z

<p>创建页面，内容为“{{Infobox nodebasic |nodename=PLS回归 |nodeimage=PLS Regression.png |icon=PLS Regression.svg |simpleicon=PLS Regression_Pure.svg |developer=Dev.Team-DPS |productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用 |productionstatedesc=在<a href="/index.php?title=Update:DecisionLinnc_1.0.2.0&amp;action=edit&amp;redlink=1" class="new" title="Update:DecisionLinnc 1.0.2.0（页面不存在）">V1.0.2</a>部署 |nodeenglishname=<a href="/index.php?title=Has_english_name::PLS_Regression&amp;action=edit&amp;redlink=1" class="new" title="Has english name::PLS Regression（页面不存在）">Has english name::PLS Regression</a> |abbreviation=<a href="/index.php?title=Has_abbreviation::PLSReg&amp;action=edit&amp;redlink=1" class="new" title="Has abbreviation::PLSReg（页面不存在）">Has abbreviation::PLSReg</a> |funcmaincategory=数据分析 |funcsubcategory=DataAGM Lv…”</p> <p><b>新页面</b></p><div>{{Infobox nodebasic <br /> |nodename=PLS回归<br /> |nodeimage=PLS Regression.png<br /> |icon=PLS Regression.svg<br /> |simpleicon=PLS Regression_Pure.svg<br /> |developer=Dev.Team-DPS<br /> |productionstate={{图标文件|Win}} / {{图标文件|W10}} Win10及以上可用<br /> |productionstatedesc=在[[Update:DecisionLinnc 1.0.2.0|V1.0.2]]部署<br /> |nodeenglishname=[[Has english name::PLS Regression]]<br /> |abbreviation=[[Has abbreviation::PLSReg]]<br /> |funcmaincategory=数据分析<br /> |funcsubcategory=[[DataAGM Lv1 Cat::回归分析]]<br /> |nodecategory=数据挖掘<br /> |nodeinterpretor=R<br /> |nodeshortdescription=&lt;p&gt;PLS回归又称偏最小二乘回归,是一种解决共线性问题,多个因变量Y同时分析,以及处理小样本时影响关系研究的一种多元统计方法。通过投影分别将预测变量和观测变量投影到一个新空间，来寻找一个线性回归模型。用于查找两个矩阵(X和Y)的基本关系，即一个在这两个空间对协方差结构建模的隐变量方法。模型将试图找到X空间的多维方向来解释Y空间方差最大的多维方向。回归适合当预测矩阵比观测的有更多变量,以及X的值中有多重共线性的时候。&lt;/p&gt;&lt;p&gt;用途：用于处理预测变量多于观测值或者预测变量之间高度相关（多重共线性）的情况。&lt;/p&gt;&lt;p&gt;参数：可以输入多个数值因变量和多个数值自变量。&lt;/p&gt;<br /> |nodeinputnumber=4<br /> |nodeoutputnumber=2<br /> |nodeloopsupport=是<br /> |nodeifswitchsupport=否<br /> |nodeavailableplotlist=SplittingLinePlot;SplittingPointPlot<br /> |nodeavailabletablelist=Coeff-Value;SE;t-Value;P-Value;R-Square;RMSEP;MSEP<br /> |nodeconfiguration=VariableList;DropMenu;Text<br /> |nodeinputports=WorkFlow-Control ➤;Transfer-Variable ◆;Transfer-Table ■<br /> |nodeoutputports=WorkFlow-Control ➤;Transfer-Table ■<br /> |statsapewikiurl=https://wiki.statsape.com/PLS回归<br /> |previousnode=[[多项式回归]]<br /> |nextnode=[[广义线性回归_逆概率加权联合标准差]]<br /> }}<br /> <br /> <br /> {{Navplate AlgorithmNodeList}}<br /> <br /> [[Category:回归分析]]</div>

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