SimMisspec-class(simsem)
SimMisspec-class()所属R语言包：simsem
Class "SimMisspec"
类“SimMisspec”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Misspecification model added on true model specifiction. This class contains SimVector, SimMatrix, and SymMatrix specifying misspecification.
误设模型添加真实的模型specifiction。这个类包含了SimVector，SimMatrix和SymMatrix指定设定错误。
类对象----------Objects from the Class----------
Object can be created by simMisspecCFA, simMisspecPath, or simMisspecSEM, for CFA, Path analysis, or SEM model, respectively. Objects can be also created by calls of the form new("SimMisspec", ...).
simMisspecCFA，simMisspecPath或simMisspecSEM，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("SimMisspec", ...)。
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: Regreeion effect from exogenous factors to endogenous factors
GA：Regreeion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
conBeforeMis: TRUE if users wish to constrain parameters before adding misspecification. FALSE if users wish to constrain parameters after adding misspecification.
conBeforeMis：TRUE，如果用户希望限制参数，然后再添加指定错误。 FALSE如果用户希望限制后，加入指定错误的参数。
misBeforeFill: TRUE if users wish to apply misspecification before applying the auto-completion on the parameters that users have not specified. FALSE if users wish to apply the auto-completion before adding misspecification. This option is helpful when users wish to apply misspecification on the parameters that users have not specified (e.g., adding trivial misspecification on the residual variance, which users let the package to calculate it and not specify it). See runMisspec for further details.
misBeforeFill：TRUE如果用户希望应用将自动完成，用户没有指定的参数设定错误之前。 FALSE如果用户希望应用的自动完成，然后再添加指定错误。用户希望应用设定错误的参数，用户没有指定（例如，增加琐碎的剩余方差的假设错误，这让包来计算的话，而不是指定）时，此选项非常有用。见runMisspec进一步的细节。
misfitType: The type of population misfit used in the misfitBound below. The default is "rmsea". The two other options are "f0" and "srmr". See popMisfitMACS for further details.
misfitType：人口不称职使用中misfitBound以下。默认的"rmsea"。其他两个选项"f0"和"srmr"。见popMisfitMACS进一步的细节。
misfitBound: The lower and upper bounds of the population misfit. This option must be a vector with two elements.
misfitBound：人口的失配的上界和下界。此选项必须是一个有两个元素的向量。
averageNumMisspec: If TRUE, the misfit will be divided by the number of free elements in the misspecification object. The default is FALSE.
averageNumMisspec：如果TRUE，失配将被划分在误设对象的免费元素由数量。默认的FALSE。
optMisfit: Use the optimization method to pick the misspecification set. That is, the program will draw a number of misspecification sets. Then, the different sets of misspecification will be compared together. If "min" is specified, the program will pick the misspecification set the provides the least amount of misfit. If "max" is specified, the program will pick the set that has the largets misfit. The default is "none" to not use the optimization method.
optMisfit：使用优化的方法来挑选误设集。也就是说，该计划将利用一些误设套。然后，将不同组的误设放在一起比较。如果"min"指定，程序将挑选误设设置提供了最少的失配。如果"max"指定，程序将挑选一套具有largets失配的。默认为"none"不使用优化方法。
numIter: The number of different misspecification sets for comparison in the optimization method.
numIter：不同的设定错误设置的优化方法进行比较。
扩展----------Extends----------
Class "SimSet", directly.
类"SimSet"，直接。
方法----------Methods----------
summary Provide the brief description of this object.
总结提供该对象的简要说明。
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。
（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 simMisspecCFA, simMisspecPath, or simMisspecSEM, respectively.
创建一个对象，这个类由终审法院，路径分析，SEM模型simMisspecCFA，simMisspecPath或simMisspecSEM“。
See how to specify true model by SimSet.
请参阅“如何指定的SimSet真实的模型。
实例----------Examples----------
showClass("SimMisspec")
n01 <- simNorm(0, 0.1)
error.cor.Mis <- matrix(NA, 6, 6)
diag(error.cor.Mis) <- 1
RTD.Mis <- symMatrix(error.cor.Mis, "n01")
CFA.Model.Mis <- simMisspecCFA(RTD=RTD.Mis)
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 09:29:35
