全傑科技 - MVSP

MVSP 執行多種類型的特徵分析排序：主成分分析 （PCA）、主座標分析 （PCO） 和對應/去趨勢對應分析 （CA/DCA）。它還進行規範對應分析（CCA），這是一種在生態學研究中非常流行的技術。

它還可以使用 23 種不同的距離或相似性度量和 7 種聚類策略來執行聚類分析。通過雙重聚類分析選項，您可以在一個步驟中生成變數和事例的樹狀圖，以及原始數據矩陣的副本，這些副本按與樹狀圖相同的順序排序。還可以執行約束聚類分析，以便保持原始輸入數據的順序。多樣性指數可以根據生態數據計算;這些包括Simpson's, Shannon's和Brillouin's indices。

可以分析的事例和變數的數量僅受 Windows 可用內存量（RAM 和硬碟交換檔）的限制，最多 20 億個事例和變數。

MVSP桌面
MVSP 使用 KCS 桌面隱喻（請參閱此螢幕截圖）。您可以在研究數據，統計結果和圖形時將其展開，就像桌面上的紙張一樣。它還有一個記事本，您可以在其中記下想法和觀察結果。嘗試新圖表，添加新數據，仔細閱讀結果，然後列印或僅保存所需的圖表。退出MVSP時，可以將所有視窗的位置和內容保存在桌面上。稍後，您可以將其還原到相同的狀態。MVSP 允許你從上次中斷的地方繼續操作。可以為不同的專案保存多個桌面。

圖形
分析數據后，您可以直接繪製結果。選擇要查看的排序軸，將繪製散佈圖。可以為 CA 結果繪製變數和事例的Joint plots 。可以生成PCA結果的Euclidean biplots （以變量作為向量），也可以產生CCA中環境變數的biplots 。還可以為PCA，PCO和CA / CCA生產Scree plots 。將自動繪製聚類分析結果的樹狀圖。這些圖表可以自定義，並保存您喜歡的設置以備將來使用。

還可以生成原始變數的散佈圖，以及匯總每個變數的箱形圖。所有圖形都具有縮放功能，可讓您放大圖形的各個部分，以便於查看。

其他特點
MVSP 提供各種資料操作功能，例如轉換、合併兩個或多個數據檔以及轉換為範圍穿透等格式。數據可以從各種格式導入和導出，包括Lotus 1-2-3、Excel、Quattro、xBase、Paradox、Cornell生態程式格式和各種純文本檔。

可以將單個數據事例分配給組。然後將組名稱列印在輸出和樹狀圖上，並將組在散佈圖上描繪為不同的符號.

提供完全可自定義的工具列。此外，數據編輯器和其他視窗具有多級撤銷功能，可讓您撤消在當前會話中所做的任何更改。

系統需求
Microsoft Windows 98/Windows NT 4 or later (including Windows ME/2000/XP/2003/Vista/7/8/10)
8 Mb RAM memory
5 Mb disk space

MVSP教育
許多人發現MVSP對教學很有用，因此現在有一個特殊的教育版本可用。這將分析較小的數據集（最多 100x100 行和列），但具有所有功能。提供非常有吸引力的網站許可證費用，以便您可以輕鬆地為每個學生提供MVSP教育版的副本。要查看價格和訂單，請轉到MVSP教育頁面。

MVSP Version 3特點
易於使用，具有現代Windows介面（可配置的工具欄，上下文功能表，簡單的功能表結構）。
許多使用者定義的選項會自動保存以供將來使用。
可儲存的桌面;您可以將當前分析會話的所有結果、圖形和註釋保存到磁碟，然後稍後將其還原以從上次中斷的位置繼續。
無限數量的變數和大小寫（僅受可用 Windows 記憶體的限制，包括 RAM 和硬碟交換檔）。

數據矩陣操作:
內建類似電子表格的數據編輯器;包括完整的多級撤銷功能，行和列刪除和插入。
矩陣的轉置。
數據變換，使用對數以 10、e 和 2 為底的平方根、Aitchison 對數的百分比數據和標準化。可以選擇單個變數進行轉換。
通過地層學研究的格式轉換為範圍。
可以將個別案件分配給預先指定的組;然後，這些將顯示在結果和圖表上。
將多個數據檔合併為一個。
數據匯入和匯出; Lotus 1-2-3 and Symphony, Excel, Quattro, xBase, Paradox, SIMSTAT, plain text and Cornell Ecology程式。
通過使用「導入預覽」對話框簡化了導入過程;允許您預覽導入的數據並更改選項，以確保成功的結果。

分析:
輕鬆選擇要包含在分析中的變數和案例;無需修改原始數據。
主成分分析，具有以下選項：相關或協方差矩陣，居中或無中心分析，用戶定義的要提取的軸數，包括Kaiser和Jolliffe的平均特徵值規則，用戶定義的精度水準。
主座標分析，使用以下選項執行：使用任何類型的輸入相似性矩陣，用戶定義的軸數提取和精度級別。
對應分析，具有以下選項：Hill 分段去趨勢、循環雅可比或倒數平均算法的選擇、稀有或常見分類群的加權和比例縮放、用戶定義的要提取的軸數和準確度級別、選擇用於表示案例的替代比例與變量。
典型對應分析，一種在生態學研究中非常流行的技術，用於將環境變數納入物種分佈的排序中。
二十三種不同的相似性和距離度量，包括Euclidean, squared Euclidean, standardized Euclidean, cosine theta (or normalized Euclidean), Manhattan metric, Canberra metric, Bray Curtis, chord, squared chord, chi-square, average, 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 coefficient; Sørensen’s, Jaccard’s, simple matching, Yule’s, Nei’s and Baroni-Urbani Buser's binary coefficients.
聚類分析，具有以下選項：七種策略（ UPGMA, WPGMA, median, centroid, nearest and farthest neighbour, and minimum variance (or Ward's) ），保持輸入順序的約束聚類（例如地層學研究），隨機輸入順序，積分樹狀圖生成。變數和案例的雙重聚類，並生成排序的數據矩陣;允許在數據中看到模式。
多樣性指數，包括以下選項： Simpson’s, Shannon’s或Brillouin’s指數，對數基數的選擇，均勻度和物種數量也計算在內。

圖形
原始數據中變數的散佈圖（2-d 和 3-d）。
原始數據的箱形圖。
PCA、PCO 和 CA/CCA 結果的散佈圖（2-d 和 3-d）。
CA/CCA 結果的Joint plots（變數和事例的散佈圖）。
PCA 結果的Euclidean biplots（將變數繪製為向量的個案散佈圖）。
CCA biplots，以環境變數作為向量，或將名義變數作為質心。
來自 PCA、PCO 和 CA/CCA 結果的特徵值的Scree plots 。
散佈圖上的點可以通過按兩下點來識別。還可以將標籤應用於所有點。
將事例分配給組時，散點圖將為每個組顯示不同的符號。使用的符號和顏色是用戶可定義的。
聚類結果的樹狀圖（基於圖形和文本）。
放大圖表以更仔細地查看特定區域。
完全可定製;可以修改字體，標題，顏色，背景樣式，軸縮放和位置，散點圖符號的類型和顏色。保存所有設置以備將來使用。
將圖形另存為 BMP 或 WMF 檔，或複製到 Windows 剪貼簿以傳輸到其他程式。

專業的多變量統計軟體,用於生態,地質,社會科學及市場分析等.提供23種距離和相似度的測量方法及7種分類的方法EigenSystem特徵分析

PCA;principal components  analysis ,主成分分析

PCoA; principal coordinates  analysis ,主座標分析

CA ; correspondence ,對應分析

DCA ; detrended correspondence analysis ,對應分析(去除趨勢)

CCA ; canonical correspondence analysis ,典型相關分析

Cluster analysis , 聚類分析

等等.

相關手冊下載

軟體試用版下載

使用MVSP軟體台灣之應用及研究範例:

台灣地區高甲基化兒茶素茶樹樹種篩選及其種質資源基因歧異度研究
大葉大學

台灣淡水河口仔稚魚之群聚結構及生命條碼在仔稚魚鑑種上之應用
國立中山大學

合歡山地區不同棲地類型之蝶類群聚研究
國立嘉義大學

太魯閣國家公園砂卡礑溪水棲昆蟲群聚結構研究
國立花蓮教育大學

台灣細本葡萄莖葉組織解剖特徵之種內變異性
中興大學

南台灣墾丁周邊海域仔魚群聚之時空分布及DNA生命條碼在鑑種上之應用
國立中山大學

嘉南平原稻作區的鳥類群聚與鳥害探討
國立嘉義大學

台灣鼠尾草屬羽狀複葉群之系統分類學研究
國立臺南大學

以生化及分生指標解析臺灣產現生重要針葉樹種親緣關係
中興大學

阿里山地區阿里山山椒魚食性與棲地利用之研究
國立嘉義大學

溪頭地區北勢溪水棲昆蟲群聚結構及功能組成
臺灣大學

台灣魚腥草品系 (Houttuynia cordata Thunb.) 根、莖與葉片組織解剖性狀之研究
國立中興大學

香山溼地大型底棲無脊椎動物群聚之時空變異
國立新竹教育大學

台灣栽培蒙古黃耆（Astragalus mongholicus）與多序岩黃耆（Hedysarum polybotrys）之鑑別研究
國立中興大學

基隆河上游水棲昆蟲在環境衝擊下其群聚結構、序列性、循環性 與歧異度之變化
臺灣大學

臺灣產碎雪草屬植物之系統生物研究
臺灣大學

水稻品種間炊飯特性與食味值表現之探討
國立嘉義大學

落花生種原農藝性狀之變異及利用分子標誌做為輔助選種指標之探討
國立嘉義大學

核二廠溫排水對附近水域魚類之群聚結構、食性及死亡率之影響
國立海洋大學

大鵬灣潟湖魚類群聚時空變化及其生態區位之研究
國立海洋大學

Designed and written by Warren Kovach

MVSP performs several types of eigenanalysis ordinations: principal components analysis (PCA), principal coordinates analysis (PCO), and correspondence/detrended correspondence analysis (CA/DCA). It also does canonical correspondence analysis (CCA), a technique highly popular in ecological studies.

It can also perform cluster analysis, using 23 different distance or similarity measures and seven clustering strategies. A dual clustering option lets you produce dendrograms of both variables and cases in one step, along with a copy of the original data matrix, sorted in the same order as the dendrograms. Constrained clustering may also be performed, so that the original input data order is maintained. Diversity indices may be calculated on ecological data; these include Simpson's, Shannon's, and Brillouin's indices.

The number of cases and variables that can be analyzed is limited only by the amount of memory available to Windows (RAM and hard disk swap file), up to a maximum of 2 billion cases and variables.

A full list of MVSP 3 features can be viewed here.

MVSP Desktop

MVSP uses the KCS desktop metaphor (see this screen shot). You can spread out your data, the statistical results and graphs in front of you while you study them, just like paper on your desktop. It also has a notepad where you can jot down ideas and observations. Try new graphs, add new data, peruse the results, then print or save just those that you need. When you exit MVSP you can save the position and contents of all the windows on your desktop. Later you can restore it to the same state. MVSP lets you pick up where you left off. Multiple desktops can be saved for different projects.

Graphics

Once your data have been analyzed you can plot the results directly. Select the ordination axes you want to see and scattergrams will be drawn. Joint plots of both variables and cases can be drawn for CA results. Euclidean biplots of PCA results (with variables as vectors) can be produced, as can biplots of the environmental variables in CCA. Scree plots can also be produced for PCA, PCO and CA/CCA. Dendrograms of the cluster analysis results are drawn automatically. These graphs can be customized, with your favourite settings being saved for future use.

Scatterplots of the original variables can also be produced, as well as box and whisker plots summarizing each variable. All graphs have a zoom feature that lets you magnify parts of the graph for easier viewing.

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 Version 3

• 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.
• Saveable 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:
  1. Built in spreadsheet-like data editor; includes full multilevel undo capabilities, row and column deletion and insertion.
  2. Transposition of matrix.
  3. 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.
  4. Conversion to range through format for stratigraphic studies.
  5. Can assign individual cases to pre-designated groups; these are then shown on results and graphs.
  6. Merging of several data files into one.
  7. Data import and export; Lotus 1-2-3 and Symphony, Excel, Quattro, xBase, Paradox, SIMSTAT, plain text and Cornell Ecology Programs.
  8. 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:
  1. Easy selection of variables and cases to include in analysis; no need to modify original data.
  2. 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 accuracy level.
  3. Principal Coordinates Analysis, performed with the following options: use any type of input similarity matrix, user defined number of axes to extract and accuracy level.
  4. Correspondence Analysis, with these options: Hill’s detrending by segments, choice of cyclic Jacobi or reciprocal averaging algorithm, weighting of rare or 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.
  5. Canonical Correspondence Analysis, a technique highly popular in ecological studies for incorporating environmental variables into an ordination of species distributions.
  6. 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, squared chord, chi-square, average, 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 coefficient; Sørensen’s, Jaccard’s, simple matching, Yule’s, Nei’s and Baroni-Urbani Buser's binary coefficients.
  7. Cluster analysis, with the following options: seven strategies (UPGMA, WPGMA, median, centroid, nearest and farthest neighbour, and minimum variance (or Ward's)), 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.
  8. Diversity indices, with the following options: Simpson’s, Shannon’s, or Brillouin’s indices, choice of log base, evenness and number of species also calculated.
• Graphics
  1. Scatterplots (2-d and 3-d) of variables in raw data.
  2. Box and whisker plots of raw data.
  3. Scatterplots (2-d and 3-d) of PCA, PCO and CA/CCA results.
  4. Joint plots (scatterplot of variables and cases together) for CA/CCA results.
  5. Euclidean biplots (scatterplot of cases with variables plotted as vectors) of PCA results.
  6. CCA biplots, with environmental variables as vectors or, for nominal variables, as centroids.
  7. Scree plots of eigenvalues from PCA, PCO and CA/CCA results.
  8. Points on scatterplot can be identified by clicking on point. Also can have labels applied to all points.
  9. When cases are assigned to groups scatterplots show different symbols for each group. Symbol and color used is user definable.
  10. Dendrograms of clustering results (both graphic and text-based).
  11. Zoom in on graphs to view specific areas more closely.
  12. Fully customizable; can modify fonts, titles, colours, background style, axis scaling and placement, type and colour of scatterplot symbol. All settings saved for future use.
  13. Save graphs as BMP or WMF files, or copy to Windows clipboard for transfer to other programs.

MVSP簡介中文

MVSP簡介英文

01 Simple but Complete

02 File import

03 Edit

04 Data

05 MDK

06 PCA

07 PCA

08 Cluster UPGMA

09 Dual Dendrogram

10 Cluster Centroid

11 Cluster WPGMA

12 Cluster Median

13 Cluster Variance

14 Cluster Farthest

15 Cluster Nearest

16 Cluster Similiity

17 Cluster Distance

18 Principal Coordinates Analysis

19 Cluster Transformation

20 Correspondence Analysis

21 Canonical Correlation

21 Canonical Correlation1

22 Diversity Indices

23 TextbookP470