SimAnSummary(SimultAnR)
SimAnSummary()所属R语言包：SimultAnR
Summary of Simultaneous Analysis
同时分析摘要
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function summarizes the results of SimAn.
这个函数SimAn总结了结果。
用法----------Usage----------
SimAnSummary(input)
参数----------Arguments----------
参数：input
The output of the simultaneous analysis
的输出的同时分析
Details
详细信息----------Details----------
The function SimAnSummary gives the detailed numerical results of the SimAn function.
的功能SimAnSummarySimAn功能给出了详细的计算结果。
In its first stage simultaneous analysis performs a simple correspondence analysis of each table, so the summary contains the separate correspondence analysis of each table as provided by the CorrAn function.
在第一阶段同时分析每个表执行一个简单的对应分析，所以每个表所提供的CorrAn功能摘要包含单独的对应分析。
The joint analysis of all the tables is performed in the second stage of the simultaneous analysis and total inertia, the eigenvalues, percentages of explained inertia and cumulated percentages of explained inertia for all dimensions are listed. The output also contains for the overall rows and for the columns of the tables, the masses, chi-squared distances and, by default restricted to the first two dimensions, projections of points on each dimension or principal coordinates, contributions of the points to the dimensions and squared correlations. For partial rows and for supplementary elements the same results are listed except for contributions of the points to the dimensions.
联合分析的所有表中执行的同时分析的第二阶段，和总惯量，特征值，百分比解释的惯性和累积百分比可用于所有尺寸的说明惯性被列出。输出还包含整体的行和列的表，群众，卡方距离，默认情况下，仅限于前两个维度，每个维度或主坐标点的预测上，贡献点到尺寸和平方的相关性。对于部分的行和补充元素相同的结果列的点的尺寸的捐款除外。
The output of SimAnSummary also contains the relations between overall rows and partial rows, the relations between the factors of the CA of the different tables, the relations between the factors of the SA and the factors of the separate CA of the different tables, the projections of the tables and the contributions of each table to the principal axes.
的输出SimAnSummary还包含整体行和部分行，的因素之间的关系的不同的表，在CA的SA的因素之间的关系，和CA的不同的单独的因素之间的关系表，表和每个表主轴的预测。
值----------Value----------
Results of separate correspondence analysis of each table: <table summary="R valueblock"> <tr valign="top"><td> Total inertia </td> <td> Total inertia, as a measure of the total variance of the data table </td></tr> <tr valign="top"><td> Eigenvalues and percentages of inertia </td> <td> Eigenvalues or principal inertias and percentages of explained inertia </td></tr> <tr valign="top"><td> Output for rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension, contributions and squared correlations </td></tr> <tr valign="top"><td> Output for columns </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension, contributions and squared correlations </td></tr> <tr valign="top"><td> Output for supplementary rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr> <tr valign="top"><td> Output for supplementary columns </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr>
每个表分别对应分析结果：<table summary="R valueblock"> <tr valign="top"> <TD> Total inertia </ TD> <TD>总惯量，作为衡量总的方差数据表</ TD> </ TR> <tr valign="top"> <TD> Eigenvalues and percentages of inertia </ TD> <TD>特征值或本金惯量和百分比的解释惯性</ TD> < / TR> <tr valign="top"> <TD> Output for rows </ TD> <TD>群众，卡点，他们的平均平方距离，在每个维度的点，贡献和平方相关性的预测< / TD> </ TR> <tr valign="top"> <TD> Output for columns </ TD> <TD>群众，卡方点的距离，他们的平均预测点在每个维度，贡献和平方相关性</ TD> </ TR> <tr valign="top"> <TD> Output for supplementary rows </ TD> <TD>群众，卡方点的距离，他们的平均预测点每个维度和平方相关性</ TD> </ TR> <tr valign="top"> <TD> Output for supplementary columns </ TD> <TD>群众，卡方距离，点到他们的平均预测点，每个维度和平方相关性</ TD> </ TR>
</table> Results of simultaneous analysis of the set of tables: <table summary="R valueblock"> <tr valign="top"><td> Total inertia </td> <td> Total inertia, as a measure of the total variance of the data table </td></tr> <tr valign="top"><td> Eigenvalues and percentages of inertia </td> <td> Eigenvalues or principal inertias </td></tr> <tr valign="top"><td> Output for rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension, contributions and squared correlations </td></tr> <tr valign="top"><td> Output for columns </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension, contributions and squared correlations </td></tr> <tr valign="top"><td> Output for partial rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr> <tr valign="top"><td> Projections of tables</td> <td> Projections of each table on each dimension </td></tr> <tr valign="top"><td> Contributions of tables to SA</td> <td> Contributions of each table to the dimensions </td></tr> <tr valign="top"><td> Relation between overall and partial rows</td> <td> Relation between overall and partial rows </td></tr> <tr valign="top"><td> Relation between factors of separate CA</td> <td> Relation between factors of separate correspondence analysis </td></tr> <tr valign="top"><td> Relation between factors of CA and SA</td> <td> Relation between factors of correspondence analysis and simultaneous analysis </td></tr> <tr valign="top"><td> Output for supplementary rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr> <tr valign="top"><td> Output for supplementary partial rows </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr> <tr valign="top"><td> Output for supplementary columns </td> <td> Masses, chi-squared distances of points to their average, projections of points on each dimension and squared correlations </td></tr> </table>
</ TABLE>表的设置同时分析结果：表summary="R valueblock"> <tr valign="top"> <TD> Total inertia </ TD> <TD>总惯量，如</ TD> </ TR> <tr valign="top"> <TD> Eigenvalues and percentages of inertia </ TD> <TD>特征值或本金惯量的数据表的总方差衡量</ TD> < / TR> <tr valign="top"> <TD> Output for rows </ TD> <TD>群众，卡点，他们的平均平方距离，在每个维度的点，贡献和平方相关性的预测< / TD> </ TR> <tr valign="top"> <TD> Output for columns </ TD> <TD>群众，卡方点的距离，他们的平均预测点在每个维度，贡献和平方相关性</ TD> </ TR> <tr valign="top"> <TD> Output for partial rows </ TD> <TD>群众，卡方点的距离，他们的平均预测点每个维度和平方相关性</ TD> </ TR> <tr valign="top"> <TD> Projections of tables </ TD> <TD>预测每个表的每个维度上</ TD> </ TR > <tr valign="top"> <TD> Contributions of tables to SA </ TD> <TD>贡献的每个表的尺寸</ TD> </ TR> <tr valign="top"> <TD> Relation between overall and partial rows</ TD> <TD>行整体和局部的关系</ TD> </ TR> <tr valign="top"> <TD> Relation between factors of separate CA</ TD> <TD>关系因素之间的独立的对应分析</ TD> </ TR> <tr valign="top"> <TD> Relation between factors of CA and SA </ TD> <TD>关系因素的对应分析，同时分析</ TD> </ TR> <tr valign="top"> <TD> Output for supplementary rows </ TD> <TD>群众，卡方点的距离，他们的平均预测点，每个维度和</平方相关性TD> </ TR> <tr valign="top"> <TD> Output for supplementary partial rows </ TD> <TD>群众，卡方点的距离，他们的平均预测点，每个维度和平方相关性</ TD> </ TR> <tr valign="top"> <TD> Output for supplementary columns </ TD> <TD>群众，卡方点的距离，他们的平均预测点在每个维度方的相关性</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Amaya Zarraga, Beatriz Goitisolo
参考文献----------References----------
Goitisolo, B. (2002). El Analisis Simultaneo. Propuesta y aplicacion de un nuevo metodo de analisis factorial de tablas de contingencia. Phd thesis, Basque Country University Press, Bilbao.
Zarraga, A. & Goitisolo, B. (2002). Methode factorielle pour l analyse simultanee de tableaux de contingence. Revue de Statistique Appliquee, L, 47–70
Zarraga, A. & Goitisolo, B. (2003). Etude de la structure inter-tableaux a travers l Analyse Simultanee, Revue de Statistique Appliquee, LI, 39–60.
Zarraga, A. and Goitisolo, B. (2006). Simultaneous analysis: A joint study of several contingency tables with different margins. In: M. Greenacre, J. Blasius (Eds.), Multiple Correspondence Analysis and Related Methods, Chapman & Hall/CRC, Boca Raton, Fl, 327–350.
Zarraga, A. & Goitisolo, B. (2009). Simultaneous analysis and multiple factor analysis for contingency tables: Two methods for the joint study of contingency tables. Computational Statistics and Data Analysis, 53, 3171–3182.
参见----------See Also----------
SimAn, SimAnGraph.
SimAn，SimAnGraph。
实例----------Examples----------
data(shoplifting)
dataSA <- shoplifting
### SA without supplementary elements[＃＃SA没有补充元素]
SimAn.out <- SimAn(data=dataSA, G=2, acg=list(1:9,10:18), weight= 2,
nameg=c("M", "F"))
### Summary[＃＃摘要]
SimAnSummary(SimAn.out)
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注：
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发表于 2012-9-30 09:35:56
