simSetSEM(simsem)
simSetSEM()所属R语言包：simsem
Create a set of matrices of parameter and parameter values to generate and analyze data that belongs to SEM model
创建一组参数和参数值的矩阵生成和分析数据属于SEM模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function will create set of matrices of free parameters and parameter values that belongs to full SEM model. The requirement is to specify factor residual correlation or covariance matrix, regression coefficient matrix, factor loading matrix, and measurement error correlation or covariance matrix.
此功能将创建属于全SEM模型的参数和参数值的矩阵。要求是指定残留的相关因素或协方差矩阵，回归系数矩阵，因子载荷矩阵，测量误差的相关性或协方差矩阵。
用法----------Usage----------
simSetSEM(..., exo = FALSE)
参数----------Arguments----------
参数：...
Each element of model specification, as described in Details
型号规格的每一个元素，如Details描述
参数：exo
specify TRUE if users wish to specify both exogenous and endogenous indicators.
指定TRUE如果用户希望指定外源性和内源性两种指标。
Details
详细信息----------Details----------
The matrices and vectors in the endogenous side are
的矩阵和向量中的内源性侧
LY for factor loading matrix from endogenous factors to Y indicators (need to be SimMatrix object).
LY从内生因素的因子载荷矩阵为Y指标（需要SimMatrix对象）。
TE for measurement error covariance matrix among Y indicators (need to be SymMatrix object).
TEŸ指标的测量误差之间的协方差矩阵（需要SymMatrix对象）。
RTE for measurement error correlation matrix among Y indicators (need to be SymMatrix object).
RTEŸ指标的测量误差之间的相关系数矩阵（需要SymMatrix对象）。
BE for regression coefficient matrix among endogenous factors (need to be SimMatrix object).
BE内生因素之间的回归系数矩阵（需要SimMatrix对象）。
PS for residual covariance matrix among endogenous factors (need to be SymMatrix object).
PS残留的内源性因素之间的协方差矩阵（需要SymMatrix对象）。
RPS for residual correlation matrix among endogenous factors (need to be SymMatrix object).
RPS剩余的内源性因素之间的相关系数矩阵（需要SymMatrix对象）。
VTE for measurement error variance of Y indicators (need to be SimVector object).
VTEY的指标的测量误差方差（需要SimVector对象）。
VY for total variance of Y indicators (need to be SimVector object). NOTE: Either measurement error variance or indicator variance is specified. Both cannot be simultaneously specified.
VYY的指标总方差（需要SimVector对象）。注意：无论是测量误差的方差或指定指标变异。两者不能同时指定。
TY for measurement intercepts of Y indicators. (need to be SimVector object).
TYY的指标的测量拦截。 （需要SimVector对象）。
MY for overall Y indicator means. (need to be SimVector object). NOTE: Either measurement intercept of indicator mean can be specified. Both cannot be specified simultaneously.
MY整体Y指标的手段。 （需要SimVector对象）。注意：无论哪种测量指标平均截距可以被指定。两者不能同时指定。
VPS for residual variance of endogenous factors (need to be SimVector object).
VPS剩余的内生因素的方差（需要SimVector对象）。
VE for total endogenous factor variance (need to be SimVector object). NOTE: Either total endogenous factor variance or residual endogenous factor variance is specified. Both cannot be simultaneously specified.
VE总的内源性因素方差（需要SimVector对象）。注意：无论是内源性因素方差或残留的内源性因素方差被指定。两者不能同时指定。
AL for endogenous factor intercept (need to be SimVector object).
AL内源性因素截取（需要SimVector对象）。
ME for total mean of endogenous factors (need to be SimVector object). NOTE: Either endogenous factor intercept or total mean of endogenous factor is specified. Both cannot be simultaneously specified.
ME总平均的内生因素（需要SimVector对象）。注意：无论是内源性因素拦截或指定的内生因素的总平均。两者不能同时指定。
There are four required matrices for the specification in the endogenous side only: LY, RTE (or TE), BE, and RPS (or PS). If users need to specify exogenous variable too ("exo=TRUE"), these matrices and vectors are available:
有四个必需的矩阵中的内源性侧的规范：LY，RTE（TE），BE，和RPS（PS）。如果用户需要指定外生变量（"exo=TRUE"），这些矩阵和向量：
LX for factor loading matrix from exogenous factors to X indicators (need to be SimMatrix object).
LX从外生因素对X指标的因子载荷矩阵（需要SimMatrix对象）。
TD for measurement error covariance matrix among X indicators (need to be SymMatrix object).
TDX定指标的测量误差协方差矩阵（需要SymMatrix对象）。
RTD for measurement error correlation matrix among X indicators (need to be SymMatrix object).
RTDX定指标的测量误差相关矩阵（需要SymMatrix对象）。
GA for regression coefficient matrix among exogenous factors (need to be SimMatrix object).
GA外生因素之间的回归系数矩阵（需要SimMatrix对象）。
PH for residual covariance matrix among exogenous factors (need to be SymMatrix object).
PH剩余的外生因素之间的协方差矩阵（需要SymMatrix对象）。
RPH for residual correlation matrix among exogenous factors (need to be SymMatrix object).
RPH剩余的外生因素之间的相关系数矩阵（需要SymMatrix对象）。
VTD for measurement error variance of X indicators (need to be SimVector object).
VTD的X指标的测量误差方差（需要SimVector对象）。
VX for total variance of X indicators (need to be SimVector object). NOTE: Either measurement error variance or indicator variance is specified. Both cannot be simultaneously specified.
VX的X指标总方差（需要SimVector对象）。注意：无论是测量误差的方差或指定指标变异。两者不能同时指定。
TX for measurement intercepts of Y indicators. (need to be SimVector object).
TXY的指标的测量拦截。 （需要SimVector对象）。
MX for overall Y indicator means. (need to be SimVector object). NOTE: Either measurement intercept of indicator mean can be specified. Both cannot be specified simultaneously.
MX整体Y指标的手段。 （需要SimVector对象）。注意：无论哪种测量指标平均截距可以被指定。两者不能同时指定。
VPH or VK for total exogenous factor variance (need to be SimVector object).
VPH或VK总的外源性因素方差（需要SimVector对象）。
KA or MK for total mean of exogenous factors (need to be SimVector object).
KA或MK外生因素（需要是SimVector对象“）的总平均。
TH for measurement error covariance between X measurement error and Y measurement error.
THX测量误差和Y测量误差的测量误差之间的协方差。
RTH for measurement error correlation between X measurement error and Y measurement error.
RTHX测量误差和Y测量误差的测量误差之间的相关性。
There are eight required matrices for the specification in both exogenous and endogenous sides: LY, RTE (or TE), BE, RPS (or PS), LX, RTD (or TD), GA, and RPH (or PH). If users specify the correlation/variance format (instead of the covariance format), the default specifications are
有八种必需的矩阵的规范外源性和内源性双方：LY，的RTE（或TE），BE，RPS（PS），LX，RTD（TD），GA，和RPH（PH）。如果用户指定的相关性/方差格式（代替的协方差格式），默认的规格
All indicator variances are equal to 1. Measurement error variances are automatically implied from total indicator variances.
所有指示器方差等于1。测量误差会自动从总指标的差异暗示。
All measurement error variances are free parameters.
所有的测量误差是免费的参数。
All indicator means are equal to 0. Indicator intercepts are automatically implied from indicator means.
所有指标装置是等于0。指标截距的自动隐含的指示装置。
All indicator intercepts are free parameters.
所有指标拦截的参数。
All factor variances are equal to 1.
所有因子方差等于1。
All factor variances are fixed.
所有因子方差是固定的。
All factor means are equal to 0.
所有因子的装置是等于0。
All factor means are fixed.
所有因素手段是固定的。
值----------Value----------
SimSet object that represents the SEM object. This will be used for specifying data or analysis models later.
SimSet对象代表的SEM对象。这将用于购买指定的数据或分析模型。
（作者）----------Author(s)----------
Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)
参见----------See Also----------
See class SimSet for simResult details.
请参阅类SimSetsimResult细节。
See SimMatrix, SymMatrix, or SimVector for input details.
SimMatrix，SymMatrix或SimVector输入的信息。
Use simSetCFA to specify CFA model and use simSetPath to specify path analysis model.
使用simSetCFA到指定CFA模型，并使用simSetPath指定路径分析模型。
实例----------Examples----------
u35 <- simUnif(0.3, 0.5)
u68 <- simUnif(0.6, 0.8)
n65 <- simNorm(0.6, 0.05)
loading <- matrix(0, 8, 3)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loading[7:8, 3] <- NA
loading.start <- matrix("", 8, 3)
loading.start[1:3, 1] <- 0.7
loading.start[4:6, 2] <- 0.7
loading.start[7:8, 3] <- "u68"
LY <- simMatrix(loading, loading.start)
RTE <- symMatrix(diag(8))
factor.cor <- diag(3)
factor.cor[1, 2] <- factor.cor[2, 1] <- NA
RPS <- symMatrix(factor.cor, 0.5)
path <- matrix(0, 3, 3)
path[3, 1:2] <- NA
path.start <- matrix(0, 3, 3)
path.start[3, 1] <- "n65"
path.start[3, 2] <- "u35"
BE <- simMatrix(path, path.start)
SEM.model <- simSetSEM(BE=BE, LY=LY, RPS=RPS, RTE=RTE)
loading.X <- matrix(0, 6, 2)
loading.X[1:3, 1] <- NA
loading.X[4:6, 2] <- NA
LX <- simMatrix(loading.X, 0.7)
loading.Y <- matrix(NA, 2, 1)
LY <- simMatrix(loading.Y, "u68")
RTD <- symMatrix(diag(6))
RTE <- symMatrix(diag(2))
factor.K.cor <- matrix(NA, 2, 2)
diag(factor.K.cor) <- 1
RPH <- symMatrix(factor.K.cor, 0.5)
RPS <- symMatrix(as.matrix(1))
path.GA <- matrix(NA, 1, 2)
path.GA.start <- matrix(c("n65", "u35"), ncol=2)
GA <- simMatrix(path.GA, path.GA.start)
BE <- simMatrix(as.matrix(0))
SEM.Exo.model <- simSetSEM(GA=GA, BE=BE, LX=LX, LY=LY, RPH=RPH, RPS=RPS, RTD=RTD, RTE=RTE, exo=TRUE)
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。
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发表于 2012-9-30 09:31:51
