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  MVSP-多元变量统计分析软件

什么是MVSP?

MVSP是一种廉价但功能强大的多变量分析程序,适用于PC兼容,可执行各种排序和聚类分析。 它提供了一种简单的方法来分析您的数据,从生态学和地质学到社会学和市场研究。 MVSP正在50多个国家的数百个站点中使用。 使用MVSP的分析结果已发表在众多期刊上,包括“科学”,“自然”,“生态学”,“石油地质学杂志”和“生物地理学杂志”。

分析完数据后,您可以直接绘制结果。 选择要查看的排序轴,将绘制散点图。 聚类分析结果的树形图自动生成。 然后可以在各种输出设备上打印这些图形。

概述

  1. 数据矩阵操作:数据可以转置,转换(可用的转换包括对数基于10,e和2,平方根和百分比数据的Aitchison的对数比),转换为百分比,比例,标准分数,八度等级, 或地层研究的范围贯通格式 ,可以选择行和列进行删除
  2. 数据导入导出; Lotus 1-2-3和Symphony and Cornell Ecology Programs
  3. 主坐标分析,使用以下选项执行:使用任何类型的输入相似度矩阵,用户定义的最小特征值和准确度级别
  4. 主成分分析,具有以下选项:相关或协方差矩阵,居中或未中心分析,用户定义的最小特征值,包括Kaiser和Jolliffe的平均特征值规则,用户定义的准确度水平。
  5. 对应分析(Correspondence Analysis),具有以下选项:Hill的分段去除,特征分析或互惠平均算法的选择,稀有或常见分类的加权和缩放到百分比,用户定义的最小特征值和准确度水平。
  6. 十九种不同的相似度和距离度量,包括欧几里德,欧氏平方,标准欧几里得,余弦θ(或归一化欧几里得),曼哈顿度量,堪培拉度量,和弦,卡方,平均和平均字符差异距离; Pearson积矩相关和Spearman秩次序相关系数; 百分比相似度和高尔的一般相似系数; Sorensen的,Jaccard的,简单匹配,Yule和Nei的二进制系数。
  7. 聚类分析:七种策略(UPGMA、WPGMA、中位数、质心、最近和最远近邻、最小方差)、保持输入顺序的约束聚类(如地层研究)、随机输入顺序、整体树状图生成。单独的实用程序允许按树状图的顺序排序数据矩阵;允许在数据中看到模式。
  8. 多样性指数,具有以下选项:Simpson's,Shannon's或Brillouin指数,对数基数的选择,均匀度和物种数量也可以计算

MVSP is a program for Microsoft Windows that performs a variety of ordination and cluster
analyses. It provides an inexpensive yet easy means of analyzing your data in fields ranging from
ecology and geology to sociology and market research. MVSP is in use at thousands of sites in 69
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.

Analyses

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.

MVSP Desktop

MVSP uses the KCS desktop metaphor. 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 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.
  • 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:
    • Built in spreadsheet-like data editor; includes full multilevel 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 accuracy level.
    • 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.
    • 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.
    • Canonical Correspondence Analysis, a technique highly popular in ecological studies for incorporating environmental variables into an ordination of species distributions.
    • 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; 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, nearest and farthest 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 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:
    • 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 variables and cases together) for CA/CCA results.
    • Euclidean biplots (scatterplot of cases with variables plotted as vectors) of PCA
      results.
    • CCA biplots, with environmental variables as 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 view specific areas more closely.
    • 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.
    • Save graphs as BMP or WMF files, or copy to Windows clipboard for transfer to other programs.

System Requirements

  • 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

 

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021-50391085

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