getPowerFitNested(simsem)
getPowerFitNested()所属R语言包：simsem
Find power in rejecting nested models based on the differences in fit indices
拒绝嵌套模型拟合指数的差异的基础上，查找权力
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Find the proportion of the difference in fit indices that indicate worse fit than a specified (or internally derived) cutoffs.
查找拟合指数的差异表明适合比规定的（或内部产生）截止时间差的比例。
用法----------Usage----------
getPowerFitNested(altNested, altParent, cutoff, ...)
参数----------Arguments----------
参数：altNested
SimResult that saves the simulation result of the nested model when the nested model is FALSE.
SimResult，节省的嵌套模型的模拟结果，当嵌套的模式是FALSE。
参数：altParent
SimResult that saves the simulation result of the parent model when the nested model is FALSE.
SimResult，节省了父模型的模拟结果，当嵌套的模式是FALSE。
参数：cutoff
A vector of priori cutoffs for fit indices.
一个向量的先验的截止时间为契合度。
参数：...
Additional arguments
附加参数
值----------Value----------
List of power given different fit indices.
赋予的权力不同的拟合指数列表。
方法----------Methods----------
This method will find the the differences in fit indices from altNested and altParent that provides worse fit than the cutoff. The additional arguments are revDirec, usedFit, nVal, pmMCARval, pmMARval, condCutoff, and df, which are needed when using varying sample sizes or percent missing across replications in SimResult. The revDirec is whether to reverse a direction. The default is to count the proportion of fit indices that indicates lower fit to the model, such as how many RMSEA in the alternative model that is worse than cutoffs. The direction can be reversed by setting as TRUE. The usedFit is the vector of names of fit indices that researchers wish to get power from. The default is to get the powers of all fit indices. The nVal is the sample size value that researchers wish to find the fit indices cutoffs from. The pmMCARval is the percent missing completely at random value that researchers wish to find the fit indices cutoffs from. The pmMARval is the percent missing at random value that researchers wish to find the fit indices cutoffs from. The condCutoff is a logical. If TRUE, the cutoff is applicable only a given set of nVal, pmMCARval, and pmMARval. If FALSE, the cutoff is applicable in any values of sample size and percent missing. The df is the degree of freedom used in spline method in predicting the fit indices by the predictors. If df is 0, the spline method will not be applied.
这个方法就可以找到契合度的差异altNested和altParent，提供适合比cutoff差。额外的参数revDirec，usedFit，nVal，pmMCARval，pmMARval，condCutoff和df，这是必要的时使用不同的样本量或在重复，缺少SimResult％。 revDirec是扭转方向。默认值是数的比例拟合指数，表明适合的模型，如RMSEA的另一种模式，更糟糕的是比临界值多少。的方向是可以逆转的通过设置TRUE。 usedFit是矢量，研究人员希望得到权力的拟合指数的名称。默认是所有的拟合指数的权力。 nVal为样本，研究人员希望找到拟合指数临界值的大小值。 pmMCARval是完全随机值，研究人员希望找到拟合指数临界值的百分比失踪。 pmMARval是失踪的随机值，研究人员希望找到拟合指数临界值的百分比。 condCutoff是一个逻辑。如果TRUE，截止仅适用于一组给定的nVal，pmMCARval，pmMARval。如果FALSE，截止适用于任何值的样本的大小和百分比失踪。 df程度的自由中使用的样条方法在预测的拟合指数的预测。如果df是0，样条方法将不适用。
The details are similar to the method for altNested="SimResult", altParent="SimResult", and cutoff="vector". The cutoff argument must not be specified. Rather, the nullNested and nullParent, which are additional arguments of this method, are required. The nullNested is the SimResult that saves the simulation result of the nested model when the nested model is TRUE. The nullParent is the SimResult that saves the simulation result of the parent model when the nested model is TRUE.
类似的方法altNested="SimResult"，altParent="SimResult"和cutoff="vector"的详细信息。 cutoff参数必须不被指定。相反，nullNested和nullParent，这是额外的参数这种方法，需要。 nullNested是SimResult，节省的嵌套模型的模拟结果，当嵌套的模式是TRUE。 nullParent是SimResult，节省了父模型的模拟结果，当嵌套的模式是TRUE。
（作者）----------Author(s)----------
Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)
参见----------See Also----------
getCutoff to find the cutoffs from null model.
getCutoff找到空模型的截断。
SimResult to see how to create simResult
SimResult来看看如何创建simResult
实例----------Examples----------
## Not run: [＃不运行：]
u2 <- simUnif(-0.2, 0.2)
n1 <- simNorm(0, 0.1)
u79 <- simUnif(0.7, 0.9)
loading.null <- matrix(0, 6, 1)
loading.null[1:6, 1] <- NA
LX.NULL <- simMatrix(loading.null, 0.7)
RPH.NULL <- symMatrix(diag(1))
RTD <- symMatrix(diag(6))
CFA.Model.NULL <- simSetCFA(LY = LX.NULL, RPS = RPH.NULL, RTE = RTD)
error.cor.mis <- matrix(NA, 6, 6)
diag(error.cor.mis) <- 1
RTD.Mis <- symMatrix(error.cor.mis, "rnorm(1,0,0.1)")
CFA.Model.NULL.Mis <- simMisspecCFA(RTE = RTD.Mis)
loading.alt <- matrix(0, 6, 2)
loading.alt[1:3, 1] <- NA
loading.alt[4:6, 2] <- NA
LX.ALT <- simMatrix(loading.alt, 0.7)
latent.cor.alt <- matrix(NA, 2, 2)
diag(latent.cor.alt) <- 1
RPH.ALT <- symMatrix(latent.cor.alt, 0.7)
CFA.Model.ALT <- simSetCFA(LY = LX.ALT, RPS = RPH.ALT, RTE = RTD)
# loading.alt.mis <- matrix(NA, 6, 2)[< - 矩阵loading.alt.mis（NA，6，2）]
# loading.alt.mis[is.na(loading.alt)] <- 0[loading.alt.mis is.na（loading.alt）] < - 0]
# LX.alt.mis <- simMatrix(loading.alt.mis, "runif(1,-.2,.2)")[LX.alt.mis < - simMatrix（loading.alt.mis，“runif（1 - 0.2，0.2）”）]
# CFA.Model.alt.mis <- simMisspecCFA(LY = LX.alt.mis, RTE=RTD.Mis)[CFA.Model.alt.mis < - simMisspecCFA（LY = LX.alt.mis，RTE“=”RTD.Mis“）]
SimData.NULL <- simData(CFA.Model.NULL, 500)
SimData.ALT <- simData(CFA.Model.ALT, 500)
SimModel.NULL <- simModel(CFA.Model.NULL)
SimModel.ALT <- simModel(CFA.Model.ALT)
Output.NULL.NULL <- simResult(10, SimData.NULL, SimModel.NULL)
Output.ALT.NULL <- simResult(10, SimData.ALT, SimModel.NULL)
Output.NULL.ALT <- simResult(10, SimData.NULL, SimModel.ALT)
Output.ALT.ALT <- simResult(10, SimData.ALT, SimModel.ALT)
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, nullParent=Output.NULL.ALT)
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, cutoff=c(Chi=3.84, CFI=-0.10))
Output.NULL.NULL2 <- simResult(NULL, SimData.NULL, SimModel.NULL, n=seq(50, 500, 50))
Output.ALT.NULL2 <- simResult(NULL, SimData.ALT, SimModel.NULL, n=seq(50, 500, 50))
Output.NULL.ALT2 <- simResult(NULL, SimData.NULL, SimModel.ALT, n=seq(50, 500, 50))
Output.ALT.ALT2 <- simResult(NULL, SimData.ALT, SimModel.ALT, n=seq(50, 500, 50))
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, nullNested=Output.NULL.NULL, nullParent=Output.NULL.ALT, nVal = 250)
getPowerFitNested(Output.ALT.NULL, Output.ALT.ALT, cutoff=c(Chi=3.84, CFI=-0.10), nVal = 250)
## End(Not run)[＃（不执行）]
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注：
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发表于 2012-9-30 09:20:54
