MVSP是多变量分析软件
，用于执行各类排序和聚类分析。它为从生态学
，地质学到社会学和市场钻研等领域的数据分析提供了一种轻便的步骤。 MVSP在数百个地址使用。使用MVSP进行分析的了局已经颁发在期刊上
，蕴含“科学”
，“天然”
，“生态学”
，“石油地质学杂志”和“生物地理学杂志”。

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分析完数据后
，你能够直接绘造了局。选择要查看的排序轴
，并绘造散点图。聚类分析了局的树状图是自动天生的。这些图表能够打印在输出设备上。

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MVSP执行多种类型的根分析排序：主成分分析（PCA）
，主坐标分析（PCO）和对应/分析（CA/DCA）。他执行规范对应分析（CCA）
，这是生态学钻研中盛行的技术。您还能够使用23种分歧的距离或类似性怀抱以及7种分组战术执行聚类分析Ｄ芄环治龅陌咐捅淞康氖渴躓indows可用内存（RAM和硬盘互换文件）的限度
，至多20亿个案例和变量。

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桌面

MVSP使用KCS桌面隐喻。在进建数据
，统计了局和图表时
，您能够在自己刻下扩大它们
，就像在桌上写字一样。它还有一个笔记本
，您能够在其中写下设法和观察了局。尝试新的图形
，增长新的数据
，查抄了局
，打印或保留所需的内容。

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尝试新图形
，增长新数据
，细读了局
，而后打印或保留所需的图形。退出MVSP时
，您能够将窗口的地位和内容保留在桌面上。以来您能够将其还原到相应状态。MVSP使您能够从上次中断的处所接机？梢晕制绲南钅勘Ａ舳喔鲎烂。

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分析数据后
，您能够直接绘造了局。选择要查看的排序轴
，将绘造散点图Ｄ芄唤糜诒淞康耐夹魏陀糜贑A了局的案例组合在一路Ｄ芄惶焐鶳CA了局的欧几里德双曲线（带有变量
，例如矢量）
，以及CCA中的环境变量的双曲线。也可以为PCA
，PCO和CA/CCA创建卵石图

；鼓芄淮唇ㄔ急淞康纳⒌阃
，以及汇总变量的箱形图和髯毛图。

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描述

• 数据矩阵处置：将数据转置
  ，转换（可用的转换蕴含以10为底的对数
  ，e和2
  ，平方根
  ，Aitchison的数据）
  ，转换为比例
  ，尺度分数
  ，八度音阶或领域通过地层钻研的体式
  ，能够选择行和列删除

• 数据导入和导出：Lotus 1-2-3和Symphony和Cornell生态打算

• 主坐标分析
  ，执行以下选项：使用类型的输入度矩阵
  ，用户界说的值和度

• 主成分分析
  ，拥有以下选项：有关或协方差矩阵
  ，居中或非中心分析
  ，用户界说的值
  ，Kaiser和Jolliffe的均匀值规划
  ，用户自界说的度水平

• 对应分析
  ，拥有以下选择：Hill的细分趋向
  ，分析或倒数均匀算法的选择
  ，罕见或常见分类群的加权和缩放
  ，用户界说的值和度水平。

• 欧几里得
  ，尺度欧几里德
  ，余弦（或尺度欧几里得）
  ，曼哈顿怀抱
  ，堪培拉怀抱
  ，和弦
  ，卡方
  ，均匀和均匀字符差距等十九种分歧的度和距离怀抱。 Pearson乘积矩有关和Spearman秩有关系数;度和高尔的系数; Sorensen、Jaccard的匹配
  ，Yule和Nei的二进造系数。

• 聚类分析
  ，拥有以下选择：七种战术（UPGMA
  ，WPGMA
  ，中位数
  ，质心）
  ，约束聚类
  ，其中维持输入挨次（例如地层钻研）
  ，随机输入挨次
  ，积分树状图出产。-独立的利用法式允许数据矩阵按树状图的挨次排序;允许在数据中看到模式。

• 多样性指数有以下选项：辛普森指数
  ，香农指数或布里渊指数
  ，还能够推算出对数基数
  ，均匀度和物种数量的选择。

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MVSP是能够执行一些数值分析的法式。这些能够用于科学领域。它还能够推算几种分析法令
，蕴含主坐标
，对应/去趋向对应分析和主坐标。该法式能够执行拥有分歧距离和类似性怀抱以及聚类战术的聚类分析。借助其双沉聚类选项
，用户能够在一个步骤中天生大幼写和变量的某些树状图。原始数据矩阵能够依照与它们的树状图的挨次分列Ｄ芄恢葱性际劾
，因而能够维持原始输入数据的挨次。

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但是
，能够分析的大幼写和变量的数量限于Windows推算机（硬盘和RAM互换文件）上的内存量。MVSP提供了几种数据处置职能。这些职能蕴含转换
，归并数据文件以及转换为分歧类型的体式。数据也能够导出多种体式。

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职能：

易于使用
，拥有现代Windows界面（可配置工具栏
，高低文菜单
，菜单结构）

用于界说的选项会自动保留以备未来使用

可保留的桌面

；您能够将当前分析会话中的了局
，图形和注解保留到磁盘
，而后稍后将其还原以复原到上次中断的地位。

无限数量的变量和大幼写（受可用的Windows内存（蕴含RAM和硬盘互换文件））。

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数据矩阵处置：

内置类似电子表格的数据编纂器

；蕴含多类裁撤职能
，行和列的删除和插入。

矩阵转置

数据转换
，使用对数以10
，e和2为平方根
，对数数据的Aitchison对数和尺度化Ｄ芄谎≡窀鞲霰淞拷凶。

转换成地层领域体式

能够将个案分配给预先指定的组

；而后将这些显示在了局和图形上

将多个数据文件归并为一个

数据导入和导出

；Lotus 1-2-3和Symphony
，Excel
，Quattro
，xBase
，Paradox
，SIMSTAT
，纯文本和Cornell生态法式

通过使用“导入预览”对话框
，简化了导入过程

；使您能够预览导入的数据并更改选项以得到成功

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分析：

易于选择要纳入分析的变量和案例

；无需批改原始数据

主成分分析
，拥有以下选项：有关性或协方差矩阵
，居中或无中心分析
，用户界说的要提取的轴数
，蕴含均匀值的Kaiser和Jolloffe规定。

使用以下选项执行的主坐标分析：使用类型的输入性矩阵
，用户界说的轴数来提取和精度。

对应分析
，拥有以下选项：Hill的分段分化
，选择循环Jacobi或倒数均匀算法
，对或常见分类单元进行加权并缩放
，用户界说的要提取的轴数和精度
，用于暗示案例的代替缩放比例的选择与变量。

规范对应分析（Canonical Correspondence Analysis）是在生态学钻研中盛行的一种技术
，用于将环境变量纳入物种散布的排序。

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图形：

原始数据中变量的散点图（2-d和3-d）

原始数据的箱形图和晶须图

PCA
，PCO和CA/CCA的散点图（2-d和3-d）

CA/CCA了局的结合图（变量和案例的散点图）

欧几里德双曲线（以变量绘造为矢量的情况的散点图）的PCA了局

CCA双曲线
，环境变量为矢量
，或名义变量为质心

PCA
，PCO和CA/CCA了局的值的Scree图

散点图上的点能够通过单击点来鉴别
，也能够将标签利用于点

将案例分配给组时
，散点图为组显示分歧的符号
，用户能够使用自已界说的符号和色彩

聚类了局的树状图（基于图形和基于文本）

放大图表以更仔细的查看区域

可定造

；能够批改字体
，标题
，色彩
，布景形状
，轴缩放比例和地位
，散点图符号的类型和色彩。保留地位以供未来使用

将图形另存为BMP或WMF文件
，或复造到Windows剪贴板以传输到其他法式

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英文简介：

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.

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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.

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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.

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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.

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Features of MVSP

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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

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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.

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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.

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