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

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plotPowerFitNonNested(simsem)
plotPowerFitNonNested()所属R语言包：simsem

                                         Plot power of rejecting a non-nested model based on a difference in fit index
                                         图权力，拒绝非嵌套模型拟合指数的差异

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Plot the proportion of the difference in fit indices from one model that does not in the range of sampling distribution from another model (reject that the dataset comes from the second model) or indicates worse fit than a specified cutoff. This plot can show the proportion in the second model that does not in the range of sampling distribution from the first model too.
比例绘制一个模型，不从另一个模型（拒绝该数据集从第二个模型）的抽样分布范围，或表明适合于指定的截止差拟合指数的差异。此图可以显示第二个模型中不采样分布也从第一模型的范围内的比例。


用法----------Usage----------


plotPowerFitNonNested(dat2Mod1, dat2Mod2, dat1Mod1=NULL, dat1Mod2=NULL,
cutoff = NULL, usedFit = NULL, alpha = 0.05, contN = TRUE, contMCAR = TRUE,
contMAR = TRUE, useContour = TRUE, logistic = TRUE, onetailed = FALSE)



参数----------Arguments----------

参数：dat2Mod1
SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 2  
SimResult，节省了模拟分析模型1模型2所创建的数据集


参数：dat2Mod2
SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 2  
SimResult，节省了模拟分析模型2模型2所创建的数据集


参数：dat1Mod1
SimResult that saves the simulation of analyzing Model 1 by datasets created from Model 1  
SimResult，节省了模拟分析模型1模型1所创建的数据集


参数：dat1Mod2
SimResult that saves the simulation of analyzing Model 2 by datasets created from Model 1  
SimResult，节省了模拟分析模型2模型1所创建的数据集


参数：cutoff
A vector of priori cutoffs for the differences in fit indices.  
一个先验的截止时间为向量的拟合指数的差异。


参数：usedFit
Vector of names of fit indices that researchers wish to plot.  
矢量，研究人员希望绘制的拟合指数的名称。


参数：alpha
A priori alpha level  
先验α水平


参数：contN
Include the varying sample size in the power plot if available  
包括不同样本大小的功率变化的曲线，如果有


参数：contMCAR
Include the varying MCAR (missing completely at random percentage) in the power plot if available  
包括不同的MCAR（完全丢失在随机的百分比）在功率变化的曲线，如果有的话


参数：contMAR
Include the varying MAR (missing at random percentage) in the power plot if available  
包括在功率变化的曲线，如果有不同的MAR（失踪在随机的百分比）


参数：useContour
If there are two of sample size, percent completely at random, and percent missing at random are varying, the plotCutoff function will provide 3D graph. Contour graph is a default. However, if this is specified as FALSE, perspective plot is used.  
如果有两个样本量，％完全随机的，百分之随机缺失不同的，plotCutoff：功能将提供3D图形。等高线图是默认的。但是，如果这是指定作为FALSE，使用立体图。


参数：logistic
If logistic is TRUE and the varying parameter exists (e.g., sample size or percent missing), the plot based on logistic regression predicting the significance by the varying parameters is preferred. If FALSE, the overlaying scatterplot with a line of cutoff is plotted.  
如果logisticTRUE和不同的参数（例如，样本大小或缺少％），图的基础上logistic回归分析预测的变参数的意义是首选。如果FALSE，散点图绘制线的截止覆盖。


参数：onetailed
If TRUE, the function will use the cutoff from one-tail test. If FALSE, the funciton will use the cutoff from two-tailed test.  
如果TRUE，功能使用截止的单尾测试。如果FALSE，功能可按使用双尾检验截止。


值----------Value----------

NONE. Only plot the fit indices distributions.
NONE。仅绘制的拟合指数分布。


（作者）----------Author(s)----------



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




参见----------See Also----------

SimResult for simResult that used in this function.
SimResultsimResult中使用此功能。

getCutoffNonNested to find the cutoffs of the differences in fit indices for non-nested model comparison
getCutoffNonNested找到临界值的差异在非嵌套模型的拟合指数比较

plotCutoffNonNested to visualize the cutoffs of the differences in fit indices for non-nested model comparison
plotCutoffNonNested可视化的临界值的差异在非嵌套模型的拟合指数比较

getPowerFitNonNested to find the power in rejecting the non-nested model by the difference in fit indices cutoffs
getPowerFitNonNested找到的权力，拒绝非嵌套模型的拟合指数临界值的差异


实例----------Examples----------


## Not run: [＃不运行：]
n1 <- simNorm(0, 0.1)
u79 <- simUnif(0.7, 0.9)

loading.A <- matrix(0, 8, 2)
loading.A[1:3, 1] <- NA
loading.A[4:8, 2] <- NA
LX.A <- simMatrix(loading.A, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPH <- symMatrix(latent.cor, "u79")
RTD <- symMatrix(diag(8))
CFA.Model.A <- simSetCFA(LY = LX.A, RPS = RPH, RTE = RTD)

error.cor.mis <- matrix(NA, 8, 8)
diag(error.cor.mis) <- 1
RTD.Mis <- symMatrix(error.cor.mis, "n1")
CFA.Model.A.Mis <- simMisspecCFA(RTE = RTD.Mis)

loading.B <- matrix(0, 8, 2)
loading.B[1:4, 1] <- NA
loading.B[5:8, 2] <- NA
LX.B <- simMatrix(loading.B, 0.7)
CFA.Model.B <- simSetCFA(LY = LX.B, RPS = RPH, RTE = RTD)

SimData.A <- simData(CFA.Model.A, 500)
SimData.B <- simData(CFA.Model.B, 500)

SimModel.A <- simModel(CFA.Model.A)
SimModel.B <- simModel(CFA.Model.B)

# The actual number of replications should be greater than 10.[的实际数目的复制应该是大于10。]
Output.A.A <- simResult(10, SimData.A, SimModel.A)
Output.A.B <- simResult(10, SimData.A, SimModel.B)
Output.B.A <- simResult(10, SimData.B, SimModel.A)
Output.B.B <- simResult(10, SimData.B, SimModel.B)

plotPowerFitNonNested(Output.B.A, Output.B.B, dat1Mod1=Output.A.A, dat1Mod2=Output.A.B)
plotPowerFitNonNested(Output.B.A, Output.B.B, cutoff=c(AIC=0, BIC=0))

## End(Not run)[＃（不执行）]

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


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