RR(TripleR)
RR()所属R语言包：TripleR
Triple R: Round-Robin Analyses Using R
三R：使用R的轮转分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function RR performs Social Relation Model (SRM) analyses for single or multiple round-robin groups.
功能RR进行社会关系模型（SRM）分析单个或多个循环赛。
用法----------Usage----------
RR(formula, data, na.rm=FALSE, minData=1, verbose=TRUE, g.id=NULL, index="",
exclude.ids="", varname=NA, minVar=localOptions$minVar, ...)
参数----------Arguments----------
参数：formula
a formula (see details) consisting of a measure (e.g. a rating) obtained with a round-robin group
一个循环的公式（见详情）组成的措施（如评级）
参数：data
A data frame in long format
长格式的数据框
参数：na.rm
If missing values are present, you have to set this parameter to TRUE
如果存在遗漏值，你必须将此参数设置为TRUE
参数：minData
Sets the minimum of data points which have to be present in each row and column
设置具有最小的数据点是在每行和每列本
参数：verbose
Defines if warnings and additional information should be printed on execution
定义执行上应印有警告和其他信息
参数：g.id
For internal use only; do not set this parameter
仅供内部使用，不设置此参数
参数：index
set index = 'enhance' for additionally requesting an index for self enhancement (self rating - perceiver effect - target effect - group mean of self ratings; Kwan, John, Kenny, Bond, & Robins, 2004) along with the actor and partner effects.
设置index = 'enhance'还要求自我增值的索引（自评 - 知觉者的影响 - 靶效应 - 自我评价组平均;关，约翰·肯尼，债券，和罗宾斯，2004年）的演员和合作伙伴一起的影响。
参数：exclude.ids
For internal use only; do not set this parameter
仅供内部使用，不设置此参数
参数：varname
The name stem of the effects variables. By default, this is the first variable passed in the formula. In case of latent constructs, however, it might be preferable to set a new name for the latent construct.
干的影响的变量的名称。默认情况下，这是第一个公式中的变量传递。然而，它在潜伏的构造的情况下，可能会对优选设置一个新的名称为潜构造。
参数：minVar
Actor and partners effects are only calculated if the respective relative variance component is greater than minVar. Set minVar to NA if this cleaning should not be performed. For small groups, Kenny (1994) suggests a minimum relative variance of 10% for the interpretation of SRM effects. In any case, actor/ partner effects and correlations with these variables should not be interpreted if these components have negative variance estimates. minVar defaults to zero; with RR.style this default can be changed for all subsequent analyses.
演员和合作伙伴的影响计算，如果各自相对方差分量大于minVar的。设置minVar NA如果这不应该进行清洗。对于小团体，肯尼（1994）提出了最低10％的相对方差的解释SRM的影响。在任何情况下，不应被理解的演员/合作伙伴的影响和这些变量的相关性，如果这些组件有负面的方差估计。 minVar默认为为零;与RR.style中此默认值可以改变所有后续的分析。
参数：...
Further undocumented or internal arguments passed to the function
进一步无证或内部参数传递给函数
Details
详细信息----------Details----------
Please note: detailed instructions on how to use the TripleR package are provided in the built in pdf document "How to use TripleR". You can open this document either by on this link: ../doc/TripleR.pdf, or you can browse all included vignettes by opening the index of the package documentation (scroll down to the very end of this page and click on "Index"; than click on "Overview of user guides and package vignettes"). These help files are only for quick references.
请注意：详细说明如何使用三倍包中提供了内置的PDF文档如何使用三倍频的。您可以打开这个文件可以通过此链接：.. / DOC / TripleR.pdf的，也可以浏览所有的包文件（向下滚动到了最后这一页，然后单击打开索引，包括护身符“指数“比点击用户指南和包护身符”概述“）。这些帮助文件的快速参考。
The estimation of the parameters of the Social Relation Model (SRM) is based on formulae provided by Kenny (1994; p. 236-244). For tests of significance of a single group, Triple R computes standard errors by using formulae published by Bond and Lashley (1996) for the case of a univariate SRM analysis. The formulae for the standard errors of the bivariate SRM parameters were kindly provided to us by C.F. Bond in personal communication. If multiple groups are provided, by default a between-group t-test is employed to calculate the significance. If you have very few groups with a considerable size (n>15), even in the multiple group scenario it might be preferable to use the within-group test of significance. You can inspect the within-group test of significance for each of the multiple groups in the return value (see groups.univariate).
的社会关系模型（SRM）的参数估计的基础上提供的公式由Kenny（1994年，第236~244）。在测试中具有重要意义的一个组，三R计算标准误差通过债券和拉什利（1996年）出版的单变量SRM分析的情况下使用公式。好心的二元SRM参数的标准误差的计算公式为向我们提供的CF个人通信的债券。如果有多个组，默认情况下，组间采用t检验来计算的意义。如果您有极少数的群体，具有相当规模（N> 15），即使在多组方案，它可能是最好使用组内的显着性检验。您可以检查的显着性检验，组内的多个组中的返回值（见groups.univariate）。
The formula to specify the round robin design has following notation: DV ~ perceiver.id * target.id | group.id (group.id is only provided if multiple groups are present). If two variables are used to describe one latent construct, both are connected with a slash on the left hand side: DV1a/DV1b ~ perceiver.id * target.id. If two variables are used to describe two manifest constructs, both are connected with a + on the left hand side: DV1+DV2 ~ perceiver.id * target.id. A latent analysis of two constructs would be notated as following: DV1a/DV1b + DV2a/DV2b ~ perceiver.id * target.id.
公式来指定循环设计有以下符号：DV ~ perceiver.id * target.id | group.id（group.id如果存在多个组，只提供）。如果两个变量是用来描述一个潜在的构建体，两者都连接在左手侧带斜杠：DV1a/DV1b ~ perceiver.id * target.id。如果两个变量是用来描述两个列表结构，两者都与+上的左手侧：DV1+DV2 ~ perceiver.id * target.id连接。一个潜在的分析两种结构将被记谱如下：DV1a/DV1b + DV2a/DV2b ~ perceiver.id * target.id。
Data sets from the Mainz Freshman Study (see Back, Schmukle, & Egloff, in press) are included (liking_a, liking_b, metaliking_a, metaliking_b, likingLong), as well as an additional data set containing missing values (multiGroup)
美因茨大学新生研究的数据集（请参阅背面，Schmukle，与Egloff的，出版中）（liking_a，liking_b，metaliking_a，metaliking_b，likingLong），以及一个额外的数据集，其中包含遗漏值（多组）
The handling for missing data (na.rm=TRUE) is performed in three steps:
处理缺失数据（na.rm=TRUE）三个步骤进行：
Rows and columns which have less then minData data points are removed from the matrix (i.e. both the "missing" row or column and the corresponding column or row, even if they have data in them)
不到的行和列minData的数据点从矩阵（即两个“失踪”的行或列，即使他们有他们的数据和相应的列或行）
For the calculation of actor and partner variances, actor-partner-covariances and the respective effects, missing values are imputed as the average of the respective row and col means. The calculation of relationship variances and covariances as well as relationship effects is also based on the imputed data set; however, single relationship effects which were missing in the original data set are set to missing again.
计算的演员和合作伙伴的差异，演员合作伙伴 - 协方差和各自的作用，缺失值估算的平均各自的行和COL方式的的。的关系以及方差和协方差关系的影响也是基于计算的估算数据集，但中缺少原始数据集的单一关系的影响再次丢失。
In the case of multiple variables (i.e., latent or bivariate analyses), participants are excluded listwise to ensure that all analyses are based on the same data set.
在多个变量（即潜在的或二元分析）的情况下，参与者被排除完全排除，以确保所有的分析都是基于相同的数据集。
值----------Value----------
Printed are both unstandardized and standardized SRM estimates with the corresponding standard errors and t-values for actor variance, partner variance, relationship variance, error variance, actor-partner covariance, and relationship covariance. In case of a bivariate analysis values are additionally provided for actor-actor covariance, partner-partner covariance, actor-partner covariance, partner-actor covariance, intrapersonal relationship covariance, and interpersonal relationship covariance.
印刷是非标准和标准的的SRM估计与相应的标准误差和t值为演员方差，合作伙伴方差，关系方差，误差方差，演员合作伙伴的协方差，和关系的协方差。在双变量分析值的情况下，还提供演员，演员的协方差，合作伙伴，合作伙伴的协方差，演员合作伙伴的协方差，合作伙伴演员方差，个人内在关系的协方差，和人际交往关系的协方差。
Reliabilities of actor, partner, and relationship effects (the latter only in the case of latent analyses) are printed according to Bonito & Kenny (2010).
可靠性演员，合作伙伴和关系的影响（后者只潜伏的分析的情况下），印刷根据鲣肯尼（2010）。
The returned values depend on the kind of analyses that is performed:
返回的值依赖的种类进行分析：
Univariate, single group:
单因素，单组：
参数：effects
actor and partner effects for all participants; if self ratings are provided they also are returned as group mean centered values
所有参与者的演员和合作伙伴的影响;自我评价，他们也正在返回的组是指为本的价值观
参数：effects.gm
actor and partner effects for all participants, group mean added
演员和合作伙伴的所有参与者，集团的意思是加入
参数：effectsRel
relationship effects for all participants
所有参加者的关系的影响
参数：varComp
variance components
方差分量
Bivariate, single group:
二元，单组：
参数：univariate
List of results of univariate of SRM analyses of both variables- specify variable in index: univariate[[1]] or univariate[[2]]. That means, each of the both univariate objects is a complete RR object for the univariate analyses, nested in the results objects. If you want to retrieve the effects for the first variable, you have to type RR2$univariate[[1]]$effects. If you want to retrieve the variance components, you have to type RR2$univariate[[1]]$varComp
这两个变量指定变量的指数SRM分析的结果单因素列表：单变量[[1]]或单变量[[2]]。这意味着，每个的两个univariate对象是嵌套的结果对象的单变量分析中，一个完整的RR对象。如果你要检索的第一个变量的影响，你有，键入RR2$univariate[[1]]$effects。如果你想检索方差分量，你必须键入RR2$univariate[[1]]$varComp
参数：bivariate
Results of bivariate SRM analyses
结果二元SRM分析
In the multiple group case, following values are returned:
在多组的情况下，返回以下值：
参数：univariate
The weighted average of univariate SRM results
单变量的加权平均SRM结果
参数：bivariate
The weighted average of bivariate SRM results
二元SRM结果的加权平均
参数：groups
SRM results for each group
SRM结果各组
参数：effects
actor and partner effects for all participants
所有参与者的演员和合作伙伴的影响
参数：effectsRel
relationship effects for all participants
所有参加者的关系的影响
参数：varComp.group
a list of variance components in all single groups
在所有的单组方差分量的列表
参数：group.var
group variance estimate
组方差估计
If self ratings are present in the data set, the function also prints the correlation between self ratings and actor/partner effects. In case of multiple groups, these are corrected for group membership (partial correlations). These correlations with self-ratings can also directly be computed with the function selfCor. Partial correlations to external (non-SRM) variables can be computed with the function parCor
如果自我评价的数据集，功能上也打印自我评价之间的相关性和演员/合作伙伴的影响。在多个组的情况下，这些校正组成员（部分相关）。这些相关性与自我评价，也可以直接进行计算的功能selfCor。外部（非SRM）的变量的部分的相关性，可以计算与函数parCor
注意----------Note----------
In case that a behavior was measured, the variances of an SRM analysis are labeled as actor variance, partner variance and relationship variance (default output labels). In case that a perception was measured, perceiver is printed instead of actor and target is printed instead of partner. You can set appropriate output labels by using the function RR.style. These settings from RR.style, however, can be overwritten for each print call: :
SRM分析的差异的情况下的行为，被标记为演员方差，合作伙伴方差和关系方差（默认输出标签）。一种看法的情况下，接受主体的印刷，而不是演员的合作伙伴而不是目标。您可以设置适当的输出标签使用的功能RR.style。这些设置从RR.style，但是，可以是为每个print呼叫覆盖::
print(RRobject, measure1="behavior"): prints output for a univariate analysis of behavior data.
print(RRobject, measure1="behavior")：打印输出的单因素分析行为数据。
print(RRobject, measure1="perception"): prints output for a univariate analysis of perception data.
print(RRobject, measure1="perception")：打印输出的感知数据的单因素分析。
print(RRobject, measure1="behavior", measure2="behavior"): prints output for a bivariate analysis of behavior data.
print(RRobject, measure1="behavior", measure2="behavior")：打印输出的二元行为数据的分析。
print(RRobject, measure1="perception", measure2="perception"): prints output for a bivariate analysis of perception data.
print(RRobject, measure1="perception", measure2="perception")：打印的二元感知数据的分析输出。
print(RRobject, measure1="behavior", measure2="perception") or <br> print(RRobject, measure1="perception", measure2="behavior"): prints output for a bivariate analysis of behavior and perception data.
print(RRobject, measure1="behavior", measure2="perception")或参考print(RRobject, measure1="perception", measure2="behavior")：打印输出的二元的行为和感知数据的分析。
print(RRobject, measure1="perception", measure2="metaperception"): is for the special case that a perception and a corresponding metaperception was measured. In this case, additionally the appropriate output labels for bivariate SRM analyses of other- and metaperceptions (reciprocities, assumed reciprocities, meta-accuracies; see Kenny, 1994) are presented.
print(RRobject, measure1="perception", measure2="metaperception")是一个感知和相应metaperception的是测量的特殊情况。在这种情况下，另外的适当的输出标签有关二元SRM分析其他和metaperceptions（的互惠，假定互惠，元精度，见肯尼，1994）提出的。
You can plot any RR object using plot(RR). See plot.RRuni for possible parameters.
您可以绘制任何使用plot(RR)RR对象。见plot.RRuni可能的参数。
（作者）----------Author(s)----------
Stefan C. Schmukle, Felix D. Sch枚nbrodt & Mitja D. Back
参考文献----------References----------
<h3>See Also</h3> <code>RR.style</code>, <code>getEffects</code>, <code>plot_missings</code>, <code>long2matrix</code>, <code>matrix2long</code>, <code>plot.RRuni</code>, <code>RR.summary</code>, <code>selfCor</code>, <code>parCor</code>
实例----------Examples----------
# The example data are taken from the "Mainz Freshman Study" and consist [示例数据是从“美因茨大学新生研究”，由]
# of ratings of liking as well as ratings of the metaperception of [，喜欢的评级以及评级的metaperception]
# liking at zero-acquaintance using a Round-Robin group of 54 participants [使用一个循环组54人喜欢在零熟人]
#------------------------------------------------------------[-------------------------------------------------- ----------]
# ---- Single group --------------------------------------[----单组--------------------------------------]
#------------------------------------------------------------[-------------------------------------------------- ----------]
# Load data frame in long format - it contains 4 variables:[载入中长格式的数据框 - 它包含4个变量：]
#liking ratings indicator a (liking_a, "How likeable do you find this person?") [喜欢的评级指标（liking_a，“如何讨人喜欢的你找到这个人吗？”）]
#liking ratings indicator b (liking_b, "Would you like to get to know this person?")[喜欢的评级的指示b（liking_b，“你愿意去了解这个人吗？”）]
#metaliking ratings indicator a (metaliking_a, "How likeable does this person find you?")[metaliking评价指标（metaliking_a，“如何讨人喜欢的这个人找你吗？”）]
#metaliking ratings indicator b (metaliking_b, "Would this person like to get to know you?")[metaliking评级的指示b（metaliking_b，“像知道你这个人吗？”）]
data("likingLong")
#manifest univariate SRM analysis[明显的单变量SRM分析]
RR1 <- RR(liking_a ~ perceiver.id*target.id, data=likingLong)
#manifest bivariate SRM analysis[明显的二元SRM分析]
RR2 <- RR(liking_a + metaliking_a ~ perceiver.id*target.id, data=likingLong)
#latent (construct-level) univariate SRM analysis[潜在的（构建）元SRM分析]
RR3 <- RR(liking_a / liking_b ~ perceiver.id*target.id, data=likingLong)
#latent (construct-level) univariate SRM analysis, define new variable name for the latent construct[潜在的（构建）单因素SRM分析，定义新的变量名潜伏构造]
RR3b <- RR(liking_a / liking_b ~ perceiver.id*target.id, data=likingLong, varname="liking")
# compare:[比较：]
head(RR3$effects)
head(RR3b$effects)
#latent (construct-level) bivariate SRM analysis[潜在的（构建）二元SRM分析]
RR4 <- RR(liking_a/liking_b + metaliking_a/metaliking_b ~ perceiver.id*target.id, data=likingLong)
# prints output of the manifest univariate analysis[打印输出的明显的单因素分析]
# in terms of actor and partner variance (default output labels)[的演员和合作伙伴的方差（默认的输出标签）]
print(RR1, measure1="behavior")
# prints output of the manifest univariate analysis [打印输出的明显的单因素分析]
# in terms of perceiver and target variance (appropriate for perception data)[在观看者，目标方差（适用于感知数据）]
print(RR1, measure1="perception")
# prints output of the manifest bivariate SRM analysis appropriate [明显的二元SRM分析适当的打印输出]
# for perception-metaperception data [的感知metaperception数据]
print(RR2, measure1="perception", measure2="metaperception")
#prints output of the latent univariate SRM analysis[打印输出的潜单变量SRM分析]
# appropriate for perception data [适用于感知数据]
print(RR3, measure1="perception")
#prints output of the latent bivariate SRM analysis[打印潜二元SRM分析的输出]
# appropriate for perception-perception data [适当的知觉，感知数据]
# Note: you can use abbreviations of the strings "behavior", "perception", and "metaperception"[注意：您可以使用字符串“行为”，“感知”和“metaperception的缩写”]
print(RR4, measure1="p", measure2="p")
#------------------------------------------------------------[-------------------------------------------------- ----------]
# ---- Multiple groups --------------------------------------[----多组--------------------------------------]
#------------------------------------------------------------[-------------------------------------------------- ----------]
# data("multiLikingLong") is a variant of the liking data set (see above) with multiple groups[的数据（“multiLikingLong”）是一个变体，喜欢的数据集（见上文），与多个组]
data("multiLikingLong")
# set RR.style to "perception" (affects subsequent printing of objects)[设置RR.style“感知”（影响后续的打印对象）]
RR.style("perception")
#manifest univariate SRM analysis[明显的单变量SRM分析]
RR1m <- RR(liking_a ~ perceiver.id*target.id|group.id, data=multiLikingLong)
#manifest bivariate SRM analysis[明显的二元SRM分析]
RR2m <- RR(liking_a + metaliking_a ~ perceiver.id*target.id|group.id, data=multiLikingLong)
#latent (construct-level) univariate SRM analysis[潜在的（构建）元SRM分析]
RR3m <- RR(liking_a / liking_b ~ perceiver.id*target.id|group.id, data=multiLikingLong)
#latent (construct-level) bivariate SRM analysis[潜在的（构建）二元SRM分析]
RR4m <- RR(liking_a/liking_b + metaliking_a/metaliking_b ~ perceiver.id*target.id|group.id, data=multiLikingLong)
# prints output of the manifest univariate analysis[打印输出的明显的单因素分析]
# in terms of actor and partner variance (default output labels)[的演员和合作伙伴的方差（默认的输出标签）]
print(RR1m, measure1="behavior")
# prints output of the manifest univariate analysis [打印输出的明显的单因素分析]
# in terms of perceiver and target variance (appropriate for perception data)[在观看者，目标方差（适用于感知数据）]
print(RR1m, measure1="perception")
#------------------------------------------------------------[-------------------------------------------------- ----------]
# ---- Multiple groups with missing values --------------------------------------[----多组具有缺失值--------------------------------------]
#------------------------------------------------------------[-------------------------------------------------- ----------]
# a multi group data set with two variables:[两个变量设置了多组数据：]
# ex = extraversion ratings, and ne = neurotizism ratings[前外向性收视率，和ne = neurotizism评级]
data("multiGroup")
#manifest univariate SRM analysis, data set with missings[明显的单变量SRM分析的数据集，missings]
RR1miss <- RR(ex~perceiver.id*target.id|group.id, data=multiGroup, na.rm=TRUE)
#manifest univariate SRM analysis, data set with missings, [明显的单变量SRM分析，数据集与missings，]
# minimum 10 data points are requested for each participant[为每名参与者被要求最低10个数据点]
RR1miss <- RR(ex~perceiver.id*target.id|group.id, data=multiGroup, na.rm=TRUE, minData=10)
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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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 12:13:50
