SimAn(SimultAnR)
SimAn()所属R语言包：SimultAnR
Simultaneous Analysis
同时分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Simultaneous analysis is a factorial method developed for the joint treatment of a set of several data tables, especially frequency tables whose row margins are different, for example when the tables are from different samples or different time points, without modifying the internal structure of each table. In the data tables rows must refer to the same entities, but columns may be different.
同时分析是一个阶乘的联合处理几个数据表，尤其是频率表，其行边距是不同的一组开发的方法，例如，当表是从不同的样品或不同的时间点，而无需修改每个表的内部结构的。在数据表中的行必须参考相同的实体，但可能会有不同的列。
用法----------Usage----------
SimAn(data, G, acg, weight = 2, nameg = NA, sr = NA, sc = NA,
nd = 2, dp = 2)
参数----------Arguments----------
参数：data
Data set
数据集
参数：G
Number of tables to be jointly analyzed
共同分析表
参数：acg
List of number of the active columns for each table
活性的列的数量为每个表中的名单
参数：weight
Weighting on each table
对每个表的权重
参数：nameg
Prefix for identifying partial rows and tables
识别部分行和表的前缀
参数：sr
Indices of supplementary rows
行补充指标
参数：sc
Indices of supplementary columns
补充列指数
参数：nd
Number of dimensions in results
在结果中的维数
参数：dp
Number of digits in results
在结果中位数号码
Details
详细信息----------Details----------
The parameter weight refers to the weighting of each table included in simultaneous analysis in order to balance the influence of each table in the joint analysis, as measured by the inertia, and to prevent the joint analysis from being dominated by a particular table. The choice of this weighting depends on the aims of the analysis and on the initial structure of the information, and different values may be used. Three values are possible, weight = 1 means no weighting , weight = 2 means that the weighting is the inverse of the first eigenvalue (square of first singular value) of each table and is given by default, and weight = 3 means that the weighting is the inverse of the total inertia of each table.
参数weight是指同时分析包括在每个表的权重，以便平衡的影响的每个表中的联合分析，测定的惯性，并防止由一个特定的被支配的联合分析表。该加权的选择取决于目标的分析和对初始结构的信息，并且可以使用不同的值。三个可能的值，weight = 1是指无加权，weight = 2是指的权重是逆每个表的第一特征值（第一个奇异值的平方），并给予默认情况下，<X >表示的加权是每个表的总惯量的倒数。
The parameter nameg allows the user to distinguish in the interpretation of the results as well as in the graphical representations which partial rows belong to each table. By default, if this parameter is not indicated, partial rows of the first table will be identified as G1 followed by the name of the row, partial rows of the second table as G2 followed by the name of the row and so on. The nameg argument also allows the different tables in the analysis to be identified.
参数nameg，使用户能够区分在对结果的解释，以及在部分行属于每个表的图形表示。默认情况下，如果该参数没有表示，第一个表中的部分行，将被认定为G1其次是行的名称，第二个表中的部分行G2随后的名字行等。 nameg参数还允许不同的表在分析中被发现。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>totalin </td> <td> Total inertia </td></tr> <tr valign="top"><td>resin </td> <td> Results of inertia </td></tr> <tr valign="top"><td>resi </td> <td> Results of active rows </td></tr> <tr valign="top"><td>resig </td> <td> Results of partial rows </td></tr> <tr valign="top"><td>resj </td> <td> Results of active columns </td></tr> <tr valign="top"><td>Fsg </td> <td> Projections of each table </td></tr> <tr valign="top"><td>ctrg </td> <td> Contribution of each table to the axes </td></tr> <tr valign="top"><td>riig </td> <td> Relation between the overall rows and the partial rows </td></tr> <tr valign="top"><td>RCACA </td> <td> Relation between separate CA axes </td></tr> <tr valign="top"><td>RCASA </td> <td> Relation between CA axes and SA axes </td></tr> <tr valign="top"><td>Fsi </td> <td> Projections of active rows </td></tr> <tr valign="top"><td>Fsig </td> <td> Projections of partial rows </td></tr> <tr valign="top"><td>Gs </td> <td> Projections of active columns </td></tr> <tr valign="top"><td>allFs </td> <td> Projections of rows and partial rows in an array format </td></tr> <tr valign="top"><td>allGs </td> <td> Projections of columns in an array format </td></tr> <tr valign="top"><td>I </td> <td> Number of active rows </td></tr> <tr valign="top"><td>maxJg </td> <td> Maximum number of columns for a table </td></tr> <tr valign="top"><td>G </td> <td> Number of tables </td></tr> <tr valign="top"><td>namei </td> <td> Names of active rows </td></tr> <tr valign="top"><td>nameg </td> <td> Prefix for identifying partial rows, tables, etc </td></tr> <tr valign="top"><td>resisr </td> <td> Results of supplementary rows </td></tr> <tr valign="top"><td>resigsr </td> <td> Results of partial supplementary rows </td></tr> <tr valign="top"><td>Fsisr </td> <td> Projections of supplementary rows </td></tr> <tr valign="top"><td>Fsigsr </td> <td> Projections of partial supplementary rows </td></tr> <tr valign="top"><td>allFssr </td> <td> Projections of rows and partial supplementary rows in an array format </td></tr> <tr valign="top"><td>Isr </td> <td> Number of supplementary rows </td></tr> <tr valign="top"><td>nameisr </td> <td> Names of supplementary rows </td></tr> <tr valign="top"><td>resjsc </td> <td> Results of supplementary columns </td></tr> <tr valign="top"><td>Gssc </td> <td> Projections of supplementary columns </td></tr> <tr valign="top"><td>allGssc </td> <td> Projections of supplementary columns in an array format </td></tr> <tr valign="top"><td>Jsc </td> <td> Number of supplementary columns </td></tr> <tr valign="top"><td>namejsc </td> <td> Names of supplementary columns </td></tr> <tr valign="top"><td>CAres </td> <td> Results of CA of each table to be used in Summary and Graph functions </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> totalin </ TD> <TD>总惯量</ TD> </ TR> <tr valign="top"> <TD>resin </ TD> <TD>结果的惯性</ TD> </ TR> <tr valign="top"> <TD> resi </ TD> <TD>结果活动行</ TD> </ TR> <tr valign="top"> <TD>resig </ TD> <TD>结果的部分行</ TD> </ TR> <TR VALIGN = “顶”> <TD>resj </ TD> <TD>结果的活动列</ TD> </ TR> <tr valign="top"> <TD>Fsg </ TD > <TD>每个表的预测</ TD> </ TR> <tr valign="top"> <TD> ctrg </ TD> <TD>贡献的每个表的轴</ TD> </ TR> <tr valign="top"> <TD> riig </ TD> <TD>关系的整体行和部分行</ TD> </ TR> <TR VALIGN =“顶部“> <TD> RCACA </ TD> <TD>独立的CA轴之间的关系</ TD> </ TR> <tr valign="top"> <TD> RCASA </ TD> <TD> CA轴之间的关系和SA轴</ TD> </ TR> <tr valign="top"> <TD>Fsi </ TD> <TD>活动行预测</ TD> < / TR> <tr valign="top"> <TD>Fsig </ TD> <TD>部分行预测</ TD> </ TR> <tr valign="top"> <TD> Gs </ TD> <TD>预测活动列</ TD> </ TR> <tr valign="top"> <TD>allFs </ TD> <TD>行预测和部分行的阵列格式</ TD> </ TR> <tr valign="top"> <TD>allGs </ TD> <TD>预测列的阵列格式</ TD> </ TR> <tr valign="top"> <TD>I </ TD> <TD>的活动行数</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>的最大数量表列</ TD> </ TR> <tr valign="top"> <TD> maxJg </ TD> <TD>数表</ TD> </ TR> <tr valign="top"> <TD>G </ TD> <TD>的活动行的名称</ TD> </ TR> <TR VALIGN =“顶部“> <TD> namei </ TD> <TD>前缀识别部分行，表，等</ TD> </ TR> <tr valign="top"> <TD>nameg </ TD> <TD>结果的补充行</ TD> </ TR> <tr valign="top"> <TD>resisr </ TD> <TD>结果的部分补充行</ TD > </ TR> <tr valign="top"> <TD>resigsr </ TD> <TD>预测的补充行</ TD> </ TR> <tr valign="top"> <TD > Fsisr </ TD> <TD>部分的补充行预测</ TD> </ TR> <tr valign="top"> <TD> Fsigsr </ TD> <TD>预测行和部分补充行的阵列格式</ TD> </ TR> <tr valign="top"> <TD>allFssr </ TD> <TD>的补充行数</ TD> < / TR> <tr valign="top"> <TD> Isr </ TD> <TD>补充行的名称</ TD> </ TR> <tr valign="top"> <TD> nameisr </ TD> <TD>结果的补充列</ TD> </ TR> <tr valign="top"> <TD> resjsc </ TD> <TD>预测补充列</ TD> </ TR> <tr valign="top"> <TD>Gssc </ TD> <TD>预测的补充列的阵列格式</ TD> </ TR> <TR VALIGN =“顶”> <TD>allGssc </ TD> <TD>补充列数</ TD> </ TR> <tr valign="top"> <TD>Jsc </ TD> <TD>辅助列的名称</ TD> </ TR> <tr valign="top"> <TD>namejsc </ TD> <TD>结果CA的每个表使用总结和图形功能</ 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----------
SimAnSummary, SimAnGraph.
SimAnSummary，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"))
### SA with supplementary rows/columns[＃＃SA与补充的行/列]
SimAn.out <- SimAn(data=dataSA, G=2, acg=list(1:8,10:17), weight= 2,
nameg=c("M", "F"), sr= 13)
SimAn.out <- SimAn(data=dataSA, G=2, acg=list(1:8,10:17), weight= 2,
nameg=c("M", "F"), sr= 13, sc=c(9,18))
### Summary[＃＃摘要]
SimAnSummary(SimAn.out)
### Graphs on screen[＃＃图在屏幕上]
SimAnGraph(SimAn.out)
### Graphs on a pdf file[＃＃图上的PDF文件]
pdf('SAGr.pdf', paper="a4r", width=12, height=9)
SimAnGraph(SimAn.out, s1=1, s2=2, screen=FALSE)
dev.off()
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-9-30 09:35:44
