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simca使用指南
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Simca-P11.0软件是做什么的答：主要用于数据分析，像我做生物的高通量测序，就用SIMCA-P进行PCA或者PLS-DA的分析等等请问谁有SIMCA-P12.0破解版（可以无延期使用，图...答：破解补丁http://www.warezomen.com/simca-p-crack-serial-keygen-download.html干嘛还要SIMCA-P呀，功能比他强的Unscrambler！同样可以实现SIMCA等算法！该软件集成多种多元统计方法及模式识别方法，主要有PCA,PLS,PLSDA,SIMCA,PCR,PLSR,K-Mean...怎样看simca-p主成分分析的loadingplots图答：先将变量标准化：egenz1=std(x1)……进行主成分分析：pcax*,mineigen(1)主成分载荷分析：estatloading,cnorm(eigen)效果分析：estatkmo（一般要大于0.7才适合做主成分分析）碎石图：screeplot主成分选择，一般选择前几个方差解释累计...simca使用指南(图2)simca使用指南(图4)simca使用指南(图6)simca使用指南(图10)simca使用指南(图12)simca使用指南(图14)simca-p11.5得分图怎么弄到word答：这些图的样式是按照软件规定的格式来设定的。但有很多研究者想要把这些图对应的数据导出来，然后做成个性化的或创新的图。那么这期的小技巧题目是：如何将这些图的数据从SIMCA-P+中导出来。在SIMCA-P+中，把所有的分析完成，包括PCA，PLS-DA等。...防抓取，学路网提供内容。==========以下对应文字版==========simca-p中q2可以是负数吗答：请问你做的是用PLS方法拟合的吗，变量那么多应该是可以筛选的吧，几百个变量那么它的多元共线性应该很严重吧，应该找个方法用SIMCA-P将无用变量筛除，可能最优建模结防抓取，学路网提供内容。FoodsBackground Data were collected consumptionpattern differentEuropean countries. examinesimilarities differencesbetween possibleexplanations. Objective understandhow foodconsumption among industrializedcountries hencefind dissimilaritiesamong countries.Hence data have been collected 20variables 16countries. datashow how many percent householdsuse 20 food items regularly. Data dataset consists 20variables differentfoods) 16observations Europeancountries). eachcountry where particularproduct completedata table, see below. goodexample organiseyour data. twosecondary observation identifiers, Location (geographic) 60.Outline dataset. data(Workset menu). PCmodel fit(Analysis menu). results(Analysis menu). Define project Start SIMCA-P newproject from FILE NEWSIMCA-P Tutorial 0BFoods Selecttype data(XLS) ALLSupported Files dataset (FOODS_update.XLS). Data can importedfrom your hard-disk networkdrive. Data can differentformats, so select onewhich AllSupported Files. examplewe have XLS-filecreated from Excel. dataset floppydisk, we recommend youfirst copy harddisk. youwant leaveopen currentproject, remove checkmark from boxClose Current Project. Note: dataset importcan locatedanywhere accessibledirectory. locatedwhere you have defined destinationdirectory. When you click Open,SIMCA-P opens ImportWizard. SIMCA-P+,mark radiobutton SIMCA-P normal project. importwizard detects emptyrow youwant row.Chose Yes. 0BFoodsSIMCA-P Tutorial SIMCA-P has tried datatable madesome settings. Observations variablesmust have primaryID canhave many secondary ID:s. primaryID must secondaryID. casewe have name countries(unique) primaryID alsounique primaryID. eachrow smallarrow changesettings. Click countrynames chose“Primary Observation ID”. availablesettings columnscan nowPrimary ID. firstcolumn excludewhich thcolumn (Geographic location CapitalLatitude) secondaryID. data(X-variables) changed.SIMCA-P Tutorial 0BFoods sameprocedure table.First row secondrow food(unique). Shift secondrow PrimaryVariable ID. firstrow excludedwhich fine.Click yougive projectname destinationdirectory. Missing values indicated.Analysis After finishing importwizard primarydataset primarydataset dataused createmodels from. Default wholedataset UV-scaling(unit variance). primarydataset whenyou want makemodels where you change observations variables,change scaling etc. primarydataset can shownchoosing menu Dataset: Open: FOODS_update. speedbutton doseveral things. youright click tableseveral options 0BFoodsSIMCA-P Tutorial SIMCA-P Tutorial 0BFoods Whendata projectwindow opens up stmodel (PCA-X unfitted). casewe want weuse menu Analysis: Autofit speedbutton calculatecomponents one eachcomponent Based crossvalidation). When summarywindow opens up showing significantcomponents. projectwindow model,double click modelrow projectwindow. R2X(cum)(fraction dataexplained after each component) Q2(cum)(cross validated R2X(cum)). R2X(fraction dataexplained eachcomponent) cumulativeR2X(cum), Q2 Q2(cum)(cross validated R2X foodvariables expected,correlated, fairlywell summarized threenew variables, scores,explaining 65% 0BFoodsSIMCA-P Tutorial modeldescribes 64.8% variation(R2(cum)) Q214.4% (bad prediction properties stcomponent describes 31,7% variation.lts from modeluse speedbutton createfour important tly. Scores LoadingsScores quickoverview resuplots direc scoreplot (upper left, t1 vs. t2), loadingplot (lower left, p1 vs. p2), DModX (distance Overviewplot (showing R2 eachvariable). relativelyhigh newvariables computed linearavian countries omCentral lor according secondaryID “Geographic location”. DModXplot shows faraway from model(projection). Statistically criticallimit (Dcrit). overviewplot shows somevariables hav R2/Q2 indicating systematic behavior. Some have low (even negative Q2) indicating low variation (consumption almost constant over all countries). ellipserepresents HotellingT2 95%confidence (see statistical appendix). scorest1 t2,one vector originalvariables goodsummary. weightscombining originalvariables calledloadings (p1 p2),see below. scoreplot shows countries.One group Scandinsecond countriesfrom thirdmore diffuse countriesfr Europe. littleodd southEurope group Tyrolregion (close Italy)has bigimpact. plotwe can use colors. Right click selectProperties tabcolors. Chose Wecould have use coding according latitude(also secondaryID) samecoloring. Loadings (seabove). plotshows which variables describe dissimilaritybetween countries. SIMCA-P Tutorial 0BFoods Scandinavianseat crisp bread, frozen fish vegetables,while southernEurope people use garlic oliveoil, centralEuropeans French)consume moredetailed interpretation loadingscan donefrom plots showing loadingsseparately. Use menu Analysis: Loadings: Column plot. Default p1 chosen.Here we can see eachvariable stcomponent. secondcomponent use uparrow loadingscalculation confidenceinterval (jack-knifing crossvalidation procedure). Third Component crossvalidation procedure gives three components loadingplots (default component vs.component keyboardarrows shift.Up plot..Plot scores(t1 vs. t3) loadings(p1 vs. p3). thirdcomponent explains 13.8% mainlyshows high consumption Tea,Jam cannedsoups mainly 0BFoodsSIMCA-P Tutorial Contribution verynice tool seedifferences between single observations, between one observation betweengroups usecontribution plots. differencesbetween observations expressed originalvariables (weighted model).Contribution oneobservation plotDouble click scoreplot (i.e. Sweden) followingplot appears. whenyou go from calculatedaverage country CrispBread Frozen Fish, Frozen Vegetables go up. Don’ over interpret plot.Look biggestcolumns. SIMCA-P Tutorial 0BFoods oneobservation observationsWhen you want newcontribution click emptyarea someware scoreplot firstchoice compareone country countries,click onecountry (i.e. Sweden) mouse(hold down left mouse button) linearound observationsyou want toolBelow Sweden southEurope group (Italy, Portugal, Austria, Spain). Consumption garlicgoes up otherfoods goes down. 10 0BFoods SIMCA-P Tutorial SIMCA-P Tutorial 0BFoods 11 Contribution onegroup anothergroup anothergroup countries,mark firstgroup othergroup casestat scoreplot t1 vs. t3 where UK Irelanddeviates from others.Mark all countries except UK twocountries. followingcontribution plot. Consumption groundcoffe goes down jamgoes up. Summary threecomponents model datasummarizes threemajor latent variables, describing mainvariation foodconsumption investigatedEuropean countries. exampleshows simplePC modeling datatable. playaround dataset. Take away observations variables,refit new models, results.SIMCA-P Tutorial 0BSpirits SpiritsBackground Complex liquid samples can characterized,compared non-selectiveanalytical method, instanceone which takes advantage samples’ability absorbvisible light. From knownorigin, predictive models can newsamples unknowncomposition. distilledspirits investigatedusing vis- spectroscopy. We JohanTrygg UmeUniversity grantingus access dataset. Objective multivariatecharacterization based spectraldata. end,spectra measured alcoholicspirits, among them whisky spiritscan clustersrelating example,product type growingproblem brewinsee whichsparkling wines (champagne cava)were differentiated using multivariatemodel mineralcontent. Chemometric methods can greatly assist identifyingincorrectly labeled fakeproducts. Gonzalez,A.G., Repetto, Camean,A.M., Differentiation sparklingwines (cava champagne)according mineralcontent, Talanta, 63, 377-382, 2004. Data eachsample (spirit), visiblespectrum (200C600 nm) acquiredusing Shimadzuspectrometer. Signal amplitude readings were taken 0.5nm intervals yielding 801 variables. were46 unique samples plus fewreplicates giving 50 observations secondaryobservation ID designates country producttype follows:XXYY where XX indicates country YYproduct type. replicatedsample. Country Origin:USa, SCotland, IReland, CAnada, FRance, ITaly, JApan. Product Type: BOurbon, BRandy, COgnac, WHisky, Single Malt (SM), BLended, RUm. One mixed (MIXT) sample alsopresent dataset. Outline threeparts. Each part separateproject SIMCA.Overview: quickoverview. Classification: How handleclassification SIMCAScaling: How usescaling Combine: How threeparts oneproject. Gonzalez,A.G., Repetto, Camean,A.M., Differentiation sparklingwines (cava champagne)according mineralcontent, Talanta, 63, 377-382, 2004. Overview firststep quickway dataImport data All new projects SIMCAstart data.Start newproject selectingFile: New Newspeed button followingwindow opens: Data can file,from ODBCdatabase (using MS Query) emptyspreadsheet. Supported file formats filelist (see UserGuide moredetailed explanation about different file formats). examplewe chose Excel2007 file called Spirits.xlsx (XML-format). Next newwindow opens up where you can select between batchtype casechose firstalternative: Click followingwindow opens (import wizard): 0BSpiritsSIMCA-P Tutorial managelabels variables.SIMCA needs primaryID youdon’t define them SIMCA createthem automatically (just numbers). Primary ID: additionyou can mark manysecondary ID: youwant (don’t need unique).All ID: plotlater SIMCAmakes owninterpretation datamatrix imported youwant makechanges use smallarrows eachcolumn followingoptions firstcolumn PrimaryID observations(all canalso secondaryID, Class ID (described later), time,Any column can excluded.Variables can also examplewe only have variableswhich defaultsetting. SIMCA-P Tutorial 0BSpirits followingoptions firstrow PrimaryID variables(all valueshere nmfrom spectra(801 variables). canalso secondaryID. Any row can particularcase SIMCA has made acceptabledirectly, we don’t have小技巧C如何从SIMCA-P+中导出scoreplot等分析图...答：这些图的样式是按照软件规定的格式来设定的。但有很多研究者想要把这些图对应的数据导出来，然后做成个性化的或创新的图。那么这期的小技巧题目是：如何将这些图的数据从SIMCA-P+中导出来。在SIMCA-P+中，把所有的分析完成，包括PCA，PLS-DA等。...防抓取，学路网提供内容。悬赏悬赏，跪求SIMCA-P12.0软件破解版，各位大侠能...答：Simca-p破解、simca12.0DEMO版、Simca13.0DEMO版破解。详情请见：http://hi.baidu.com/yelaoxiansheng/item/025f52d611dcdbea54347f97simca-p11.5得分图怎么弄到word答：这些图的样式是按照软件规定的格式来设定的。但有很多研究者想要把这些图对应的数据导出来，然后做成个性化的或创新的图。那么这期的小技巧题目是：如何将这些图的数据从SIMCA-P+中导出来。在SIMCA-P+中，把所有的分析完成，包括PCA，PLS-DA等。...simca-p中q2可以是负数吗答：请问你做的是用PLS方法拟合的吗，变量那么多应该是可以筛选的吧，几百个变量那么它的多元共线性应该很严重吧，应该找个方法用SIMCA-P将无用变量筛除，可能最优建模结果只需要几十吧。
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因本人吝啬，不愿意掏钱给人，所有事情都自己弄的，不是说自己懂的多，大家有什么不明白的可以问我，我经验比较多，我不是好学生，不爱交作
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基于可见近红外光谱与SIMCA和PLS-DA的脐橙品种识别
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代谢组学中的S-plot如何做 - 分析 - 小木虫 - 学术 科研 第一站采用simca-p11.5进行代谢组学分析,其中的S-plot怎么做啊?求指导啊!!文献中是这样的,就是如何做出这种图呢?急求!382_420.jpg|bcs|.
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用SIMCA-P做了个PLS-DA的分析，得到了这几张图，关键是怎么解释啊，这四张图分别代表什么意思啊？求大神指点！！！
第一幅图是组间差异同时可以看到组内差异，你这个分的很好，差异性显著。竖着第二幅图，我觉得是你分析错了。不应该出现这样的。应该出现的是所有代谢产物的图。横着的第二个，应该是vip值吗？看不太清楚。整体觉得分析有误。不好意思。这是我个人看法，你可以问问其他大咖。或问他qq
第四个图应该是分成了三个主成分
引用回帖:: Originally posted by metab小刘 at
第一幅图是组间差异同时可以看到组内差异，你这个分的很好，差异性显著。竖着第二幅图，我觉得是你分析错了。不应该出现这样的。应该出现的是所有代谢产物的图。横着的第二个，应该是vip值吗？看不太清楚。整体觉得 ... 我做的分析是多因变量X对多自变量Y的PLS分析,将SIMCA-P软件录入数据，我把自变量设为X，因变量设为Y。显示PLS模型，点击Analysis/Scores/Scatter plot，显示得分图 t1 vs. t2 图；点击Analysis/loading/Scatter plot，显示w*c1 vs. w*c2图。
问题1，做出的样本得分图 t1 vs. t2 图，是从自变量X中提取的还是Y中提取的成分做的样本得分图？和单独用X或者Y变量做主成分样本得分图有什么区别？
问题2，载荷图w*c1 vs. w*c2图制作中，除了w*c项目外，还可以选择（1）P/C/PC/W/W*选项，这些选项分别是做什么图的，具体哪一个是显示自变量X和因变量Y的相关性的图（就是看出哪些X与那些Y有相关性）？
Correlation scal勾上是不是看相关性的？
问题3，PLS中如果想做多自变量X和多因变量Y各自的载荷图，怎么做啊？
我想做附件中的四个图（图2 ）
能知道一下吗？老师
引用回帖:: Originally posted by senwy886 at
我做的分析是多因变量X对多自变量Y的PLS分析,将SIMCA-P软件录入数据，我把自变量设为X，因变量设为Y。显示PLS模型，点击Analysis/Scores/Scatter plot，显示得分图 t1 vs. t2 图；点击Analysis/loading/Scatter pl ... t1 vs t2是 对X变量作图，和单独用X或Y做得分图应该差不多。
楼主知道怎么分析了吗？能不能传授一下，不胜感激！
楼主请问有软件正式版本么？可以发来用用吗？
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