R语言 simsem包 SimSet-class()函数中文帮助文档(中英文对照)

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SimSet-class(simsem)
SimSet-class()所属R语言包：simsem

                                        Class "SimSet"
                                         类“SimSet”

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Set of vectors and matrices that saves free parameters and parameter values (CFA, Path analysis, or SEM)
一组向量和矩阵，节省参数和参数值（CFA，通径分析，扫描电镜）


类对象----------Objects from the Class----------

Object can be created by simSetCFA, simSetPath, or simSetSEM, for CFA, Path analysis, or SEM model, respectively. Objects can be also created by calls of the form new("SimSet", ...).
simSetCFA，simSetPath或simSetSEM，CFA，通径分析，SEM模型，分别可以创建对象。也可以创建对象通过调用的形式new("SimSet", ...)。


插槽----------Slots----------




modelType: Model type (CFA, Path, or SEM)
modelType：型号（CFA，路径，或SEM）




LY: Factor loading matrix between endogenous factors and Y indicators
LY：因子载荷矩阵内源性因素和Y指标




TE: Covariance matrix between Y measurement error
TE：Y测量误差之间的协方差矩阵




RTE: Correlation matrix between Y measurement error
Y测量误差之间的相关性矩阵RTE：




VTE: Variance of Y measurement error
VTE：Y的测量误差的方差




PS: Residual covariance of endogenous factors  
PS：剩余协方差的内生因素




RPS: Residual correlation of endogenous factors  
RPS：剩余的内生因素的相关性




VPS: Residual variances of endogenous factors
VPS：剩余的内生因素的差异




BE: Regression effect among endogenous factors
BE：回归之间的内生因素的影响




TY: Measurement intercepts of Y indicators
TY：Y的指标的测量拦截




AL: Factor intercepts of endogenous factors
AL：内源性因素的因子拦截




ME: Factor means of endogenous factors
ME：系数是指内源性因素




MY: Total Mean of Y indicators
MY的Y指标：总的平均




VE: Total variance of endogenous factors
VE：总方差的内生因素




VY: Total variance of Y indicators
VY：总方差的Y指标




LX: Factor loading matrix between exogenous factors and X indicators
LX：因子载荷矩阵之间的外生因素和X指标




TD: Covariance matrix between X measurement error
TD：X测量误差之间的协方差矩阵




RTD: Correlation matrix between X measurement error
RTD：X测量误差之间的相关性矩阵




VTD: Variance of X measurement error
VTD：X的测量误差的方差




PH: Covariance among exogenous factors
PH：外生因素之间的协方差




RPH: Correlation among exogenous factors
RPH：外源性因素之间的相关性




GA: Regression effect from exogenous factors to endogenous factors
GA：回归外源性因素的影响，内生因素




TX: Measurement intercepts of X indicators
TX的X指标：测量拦截




KA: Factor Mean of exogenous factors
KA因子的平均外生因素




MX: Total Mean of X indicators
MX的X指标：总的平均




VPH: Variance of exogenous factors
VPH：外源性因素方差




VX: Total variance of X indicators
VX：总方差的X指标




TH: Measurement error covariance between X indicators and Y indicators
TH：X指示标志和Y指标的测量误差之间的协方差




RTH: Measurement error correlation between X indicators and Y indicators
RTH：X指示标志和Y指标的测量误差之间的相关性


方法----------Methods----------




run Create a sample of parameters in this object. In other words, draw a sample from all random parameters which is represented in VirtualDist.
运行这个对象创建一个示例中的参数。换句话说，绘制一个样本随机参数在代表VirtualDist。




summary Get the summary of model specification
总结取得的型号规格概要




extract Extract elements from a simSet. There are several additional arguments. First, if yOnly is TRUE, then the result will provide only Y side. Second, y is the index of indicators in Y side to be extracted. Third, e is the index of factors in Y side to be extracted. Fourth, x is the index of the indicators in X side to be extracted. Finally, k is the index of the factors in X side to be extracted.
提取提取从simSet元素。还有几个额外的参数。首先，yOnly如果是TRUE，那么结果将只提供Y侧。其次，y是要提取的Y侧的指标指数。三，e是要提取的因素，在Y侧的索引。四，x是该指数的指标在X方要提取。最后，k是要提取的因素在X侧的索引。


（作者）----------Author(s)----------



Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)




参见----------See Also----------

Create an object this class by CFA, Path Analysis, or SEM model by simSetCFA, simSetPath, or simSetSEM, respectively.
创建一个对象，这个类由终审法院，路径分析，SEM模型simSetCFA，simSetPath或simSetSEM“。

See how to specify model misspecification by SimMisspec.
如何指定模型误SimMisspec。


实例----------Examples----------


showClass("SimSet")

loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loadingValues <- matrix(0, 6, 2)
loadingValues[1:3, 1] <- 0.7
loadingValues[4:6, 2] <- 0.7
LX <- simMatrix(loading, loadingValues)
summary(LX)

latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPH <- symMatrix(latent.cor, 0.5)

# Error Correlation Object[错误关联对象]
error.cor <- matrix(0, 6, 6)
diag(error.cor) <- 1
RTD <- symMatrix(error.cor)

CFA.Model <- simSetCFA(LX = LX, RPH = RPH, RTD = RTD)
summary(CFA.Model)
#run(CFA.Model)[运行（CFA.Model）]

CFA.Model2 <- extract(CFA.Model, y=1:3, e=1)
summary(CFA.Model2)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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