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MVSP是多變量分析軟件，用于執(zhí)行各種排序和聚類分析。它為從生態(tài)學，地質學到社會學和市場研究等領域的數據分析提供了一種簡便的方法。 MVSP正在數百個地點使用。使用MVSP進行分析的結果已經發(fā)表在許多期刊上，包括“科學”，“自然”，“生態(tài)學”，“石油地質學雜志”和“生物地理學雜志”。
分析完數據后，你可以直接繪制結果。選擇要查看的排序軸，并繪制散點圖。聚類分析結果的樹狀圖是自動生成的。這些圖表可以打印在輸出設備上。
MVSP執(zhí)行多種類型的根分析排序：主成分分析（PCA），主坐標分析（PCO）和對應/分析（CA/DCA）。他執(zhí)行規(guī)范對應分析（CCA），這是生態(tài)學研究中流行的技術。您還可以使用23種不同的距離或相似性度量以及7種分組策略執(zhí)行聚類分析?？梢苑治龅陌咐妥兞康臄盗績H受Windows可用內存（RAM和硬盤交換文件）的限制，至多20億個案例和變量。
桌面
MVSP使用KCS桌面隱喻。在學習數據，統(tǒng)計結果和圖表時，您可以在自己面前擴展它們，就像在桌上寫字一樣。它還有一個筆記本，您可以在其中寫下想法和觀察結果。嘗試新的圖形，添加新的數據，檢查結果，打印或保存所需的內容。
嘗試新圖形，添加新數據，細讀結果，然后打印或保存所需的圖形。退出MVSP時，您可以將窗口的位置和內容保存在桌面上。以后您可以將其還原到相應狀態(tài)。MVSP使您可以從上次中斷的地方接機。可以為不同的項目保存多個桌面。
分析數據后，您可以直接繪制結果。選擇要查看的排序軸，將繪制散點圖?？梢詫⒂糜谧兞康膱D形和用于CA結果的案例組合在一起。可以生成PCA結果的歐幾里德雙曲線（帶有變量，例如矢量），以及CCA中的環(huán)境變量的雙曲線。也可以為PCA，PCO和CA/CCA創(chuàng)建卵石圖。還可以創(chuàng)建原始變量的散點圖，以及匯總變量的箱形圖和胡須圖。
描述
數據矩陣處理：將數據轉置，轉換（可用的轉換包括以10為底的對數，e和2，平方根，Aitchison的數據），轉換為比例，標準分數，八度音階或范圍通過地層研究的格式，可以選擇行和列刪除
數據導入和導出：Lotus 1-2-3和Symphony和Cornell生態(tài)計劃
主坐標分析，執(zhí)行以下選項：使用任何類型的輸入度矩陣，用戶定義的值和度
主成分分析，具有以下選項：相關或協方差矩陣，居中或非中心分析，用戶定義的值，Kaiser和Jolliffe的平均值規(guī)劃，用戶自定義的度水平
對應分析，具有以下選擇：Hill的細分趨勢，分析或倒數平均算法的選擇，罕見或常見分類群的加權和縮放，用戶定義的值和度水平。
歐幾里得，標準歐幾里德，余弦（或標準歐幾里得），曼哈頓度量，堪培拉度量，和弦，卡方，平均和平均字符差距等十九種不同的度和距離度量。 Pearson乘積矩相關和Spearman秩相關系數;度和高爾的系數; Sorensen、Jaccard的匹配，Yule和Nei的二進制系數。
聚類分析，具有以下選擇：七種策略（UPGMA，WPGMA，中位數，質心），約束聚類，其中保持輸入順序（例如地層研究），隨機輸入順序，積分樹狀圖生產。-獨立的應用程序允許數據矩陣按樹狀圖的順序排序;允許在數據中看到模式。
多樣性指數有以下選項：辛普森指數，香農指數或布里淵指數，還可以計算出對數基數，均勻度和物種數量的選擇。
MVSP是可以執(zhí)行一些數值分析的程序。這些可以用于許多科學領域。它還可以計算幾種分析法則，包括主坐標，對應/去趨勢對應分析和主坐標。該程序可以執(zhí)行具有不同距離和相似性度量以及聚類策略的聚類分析。借助其雙重聚類選項，用戶可以在一個步驟中生成大小寫和變量的某些樹狀圖。原始數據矩陣可以按照與它們的樹狀圖的順序排列?？梢詧?zhí)行約束聚類，因此可以保持原始輸入數據的順序。
但是，可以分析的大小寫和變量的數量于Windows計算機（硬盤和RAM交換文件）上的內存量。MVSP提供了幾種數據處理功能。這些功能包括轉換，合并數據文件以及轉換為不同類型的格式。數據也可以導出多種格式。
功能：
易于使用，具有現代Windows界面（可配置工具欄，上下文菜單，菜單結構）
用于定義的選項會自動保存以備將來使用
可保存的桌面；您可以將當前分析會話中的結果，圖形和注釋保存到磁盤，然后稍后將其還原以恢復到上次中斷的位置。
無限數量的變量和大小寫（僅受可用的Windows內存（包括RAM和硬盤交換文件））。
數據矩陣處理：
內置類似電子表格的數據編輯器；包括多類撤消功能，行和列的刪除和插入。
矩陣轉置
數據轉換，使用對數以10，e和2為平方根，對數數據的Aitchison對數和標準化。可以選擇各個變量進行轉換。
轉換成地層范圍格式
可以將個案分配給預先指定的組；然后將這些顯示在結果和圖形上
將多個數據文件合并為一個
數據導入和導出；Lotus 1-2-3和Symphony，Excel，Quattro，xBase，Paradox，SIMSTAT，純文本和Cornell生態(tài)程序
通過使用“導入預覽”對話框，簡化了導入過程；使您可以預覽導入的數據并更改選項以獲得成功
分析：
易于選擇要納入分析的變量和案例；無需修改原始數據
主成分分析，具有以下選項：相關性或協方差矩陣，居中或無中心分析，用戶定義的要提取的軸數，包括平均值的Kaiser和Jolloffe規(guī)則。
使用以下選項執(zhí)行的主坐標分析：使用類型的輸入性矩陣，用戶定義的軸數來提取和精度。
對應分析，具有以下選項：Hill的分段分解，選擇循環(huán)Jacobi或倒數平均算法，對或常見分類單元進行加權并縮放，用戶定義的要提取的軸數和精度，用于表示案例的替代縮放比例的選擇與變量。
規(guī)范對應分析（Canonical Correspondence Analysis）是在生態(tài)學研究中流行的一種技術，用于將環(huán)境變量納入物種分布的排序。
圖形：
原始數據中變量的散點圖（2-d和3-d）
原始數據的箱形圖和晶須圖
PCA，PCO和CA/CCA的散點圖（2-d和3-d）
CA/CCA結果的聯合圖（變量和案例的散點圖）
歐幾里德雙曲線（以變量繪制為矢量的情況的散點圖）的PCA結果
CCA雙曲線，環(huán)境變量為矢量，或名義變量為質心
PCA，PCO和CA/CCA結果的值的Scree圖
散點圖上的點可以通過單擊點來識別，也可以將標簽應用于點
將案例分配給組時，散點圖為組顯示不同的符號，用戶可以使用自已定義的符號和顏色
聚類結果的樹狀圖（基于圖形和基于文本）
放大圖表以更仔細的查看區(qū)域
可定制；可以修改字體，標題，顏色，背景樣式，軸縮放比例和位置，散點圖符號的類型和顏色。保存位置以供將來使用
將圖形另存為BMP或WMF文件，或復制到Windows剪貼板以傳輸到其他程序
英文簡介：
MVSP is an inexpensive yet powerful multivariate analysis program for PC compatibles that performs a variety of ordination and cluster analyses. It provides an easy means of analyzing your data in fields ranging from ecology and geology to sociology and market research. MVSP is in use at hundreds of sites in over 50 countries. The results of analyses using MVSP have been published in numerous journals, including Science, Nature, Ecology, Journal of Petroleum Geology, and Journal of Biogeography.
Once your data have been analyzed you can plot results directly. Select the ordination axes you want to see and scattergrams will be drawn. Dendrograms of cluster analysis results are produced automatically. These graphs can then be printed on a variety of output devices.
DESCRIPTION
Data matrix manipulation: data may be transposed, transformed (transformations available include logarithms to base 10, e, and 2, square root, and Aitchison’s logratio for percentage data), converted to percentages, proportions, standard scores, octave class scale, or range through format for stratigraphic studies, and rows and columns may be selected for deletion
Data import and export; Lotus 1-2-3 and Symphony and Cornell Ecology Programs
Principal Coordinates Analysis, performed with the following options: use any type of input similarity matrix, user defined minimum eigenvalues and accuracy level
Principal Components Analysis, with the following options: correlation or covariance matrix, centered or uncentered analysis, user defined minimum eigenvalues, including Kaiser’s and Jolliffe’s rules for average eigenvalues, user defined accuracy level.
Correspondence Analysis, with these options: Hill’s detrending by segments, choice of eigenanalysis or reciprocal averaging algorithm, weighting of rare or common taxa and scaling to percentages, user defined minimum eigenvalues and accuracy level.
Nineteen different similarity and distance measures, including Euclidean, squared Euclidean, standardized Euclidean, cosine theta (or normalized Euclidean), Manhattan metric, Canberra metric, chord, chi-square, average, and mean character difference distances; Pearson product moment correlation and Spearman rank order correlation coefficients; percent similarity and Gower’s general similarity coefficient; Sorensen’s, Jaccard’s, simple matching, Yule’s and Nei’s binary coefficients.
Cluster analysis, with the following options: seven strategies (UPGMA, WPGMA, median, centroid, nearest and farthest neighbor, and minimum variance), constrained clustering in which the input order is maintained (e.g. stratigraphic studies), randomized input order, integral dendrogram production. Separate utility program allows data matrices to be sorted in the order of the dendrograms; allows patterns to be seen in the data.
Diversity indices, with the following options: Simpson’s, Shannon’s, or Brillouin’s indices, choice of log base, evenness and number of species can also be calculated.
Other Features
MVSP offers various data manipulation features, such as transformation, merging of two or more data files, and conversion to formats such as range-through. Data can be imported from and exported to a variety of formats, including Lotus 1-2-3, Excel, Quattro, xBase, Paradox, Cornell Ecology Program format and various plain text files.
Individual data cases can be assigned to groups. The group names are then printed on output and dendrograms, and the groups are depicted on scatterplots as different symbols. A fully customizable toolbar is available. Also, the data editor and other windows have multiple level undo, letting you reverse any changes you have made in the current session.
Features of MVSP
Easy to use, with modern Windows interface(configurable toolbar, context menus, simple menu structure).
Numerous user-defined options that are automatically saved for future use.
Saveble desktop;you can save all the results, graphs and notes of the current analysis session to disk, then restore them later to resume where you left off
Unlimited number of variables and cases(restricted only by available Windows memory, including both RAM and hard disk swap file).
Data matrix manipulation:
Built in spreadsheet-like data editor; includes full multievel undo capabilities, row and column deletion and insertion
Transposition of matrix
Transformation of data, using logarithms to base 10,e, and 2, square root, Aitchison's logratio for percentage data, and standardization.
Individual variables may be selected for transformation
Conversion to range through format for stratigraphic studies
Merging of several data files into one
Data import and export; Lotus 1-2-3 and Symphony, Excel, Quattro, xBase, Paradox, SIMSTAT, plain text and Cornell Ecology Programs.
Import process eased by the use of the Import Preview dialog;lets you preview the imported data and change options to ensure successful results
Analyses:
Easy selection of variables and cases to include in analysis; no need to modify original data
Principal Components Analysis, with the following options:correlation or covariance matrix, centred or uncentred analysis, user defined number of axes to extract, including Kaiser's and Jolliffe's rules for average eigenvalues, user defined number of axes to extract and accuracy level.
Correspondence Analysis, with these options:Hill's detrending by segments, choice of cyclic Jacbi or reciprocal averaging algorithm, weighting of rare of common taxa and scaling to percentages, user defined number of axes to extract and accuracy level, choice of alternative scalings for representing cases vs. variables.
Canonical Corespondence Analysis, a technique highly popular in ecological studies for incorporating environmental variables into an ordination of species distribuyions.
Twenty three different similarity and distance measures, including Euclidean, squared Euclidean, standardized Euclidean, cosine theta(or normalized Euclidean), Manhattan metric, Canberra metric, Bray Curtis, chord, aquared chord, chi-square and mean character difference distances; Pearson product moment correlation and Spearman rank order correlation coefficients; Percent similarity, modified, Morisita's similarity and Gower's general similarity coeffcient; Srensen's, Jaccard's simple matching, Yule's Nei's and Baroni-Urbani-Buser's binary coefficients.
Cluster analysis, with the following options: seven strategies(UPGMA, WPGMA,median, centroid mearest and fathest neighbour, and minimum variance), constrained clustering in which the input order is maintained(e.g. stratigraphic studies), randomized input order, integral dendrogram production. Dual clustering of both variables and cases with a sorted data matrix being produced; allows patterns to be seen in the data.
Diversity incices, with the following options:Simpson's Shannon's or Brillouin;s indices, choice of log base, evenness and number of species also calculated.
Graphcs:
Scatterplots(2-d and 3-d) of variables in raw data
Box and whisker plots of raw data
Scatterplots(2-d and 3-d) of PCA, PCO and CA/CCA results
Joint plots(scatterplot of cases with variables plotted as vectors) of PCA results
CCA biplots, with environmental variables ad vectors or, for nominal variables, as centroids
Scree plots of eigenvalues from PCA, PCO and CA/CCA results
Dendrograms of clustering results (both graphic and text-based)
Points on scatterplot can be identified by clicking on point. Also can have labels applied to all points
Zoom in on graphs to views specific areas more closely
Fully customizable: can modify, titles, coloues, background style, axis scalling and placement, type and colour of scatterplot symbol. All settings saved for future use
Save graphs as BMP or WMF files, or copy to windows clipboard for transfer to other programs.
