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

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

                                         Find the likelihood ratio (or Bayes factor) based on the bivariate distribution of fit indices
                                         找到的可能性比（或贝叶斯因子）的基础上的二元分布的拟合指数

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Find the log-likelihood of the observed fit indices on Model 1 and 2 from the real data on the bivariate sampling distribution of fit indices fitting Model 1 and Model 2 by the datasets from the Model 1 and Model 2. Then, the likelihood ratio is computed (which may be interpreted as posterior odd). If the prior odd is 1 (by default), the likelihood ratio is equivalent to Bayes Factor.
找到二元抽样分布的拟合指数拟合的数据集的模式1和模式2模式1和模式2的真实数据模型1和2所观察到的拟合指数的对数似然。然后，似然比计算（它可被解释为后的奇数）。如果事先奇怪的是1（默认情况下），似然比贝叶斯因子。


用法----------Usage----------


likRatioFit(outMod1, outMod2, dat1Mod1, dat1Mod2, dat2Mod1, dat2Mod2,
usedFit=NULL, prior=1)



参数----------Arguments----------

参数：outMod1
SimModelOut that saves the analysis result of the first model from the target dataset  
SimModelOut，节省的第一个目标数据集模型的分析结果


参数：outMod2
SimModelOut that saves the analysis result of the second model from the target dataset  
SimModelOut，节省目标数据集的第二个模型的分析结果


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：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：usedFit
Vector of names of fit indices that researchers wish to getCutoffs from. The default is to getCutoffs of all fit indices.  
矢量的拟合指数，研究人员希望getCutoffs的名字。默认值是拟合指数的所有getCutoffs。


参数：prior
The prior odds. The prior probability that Model 1 is correct over the prior probability that Model 2 is correct.  
在之前的赔率。先验概率模型1以上的先验概率是正确的模式2是正确的。


值----------Value----------

The likelihood ratio (Bayes Factor) in preference of Model 1 to Model 2. If the value is greater than 1, Model 1 is preferred. If the value is less than 1, Model 2 is preferred.
似然比（贝叶斯因子）优先模式1到模式2。如果该值大于1时，模式1是优选的。如果该值小于1，模型2是优选的。


（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 a detail of simResult pValueNested for a nested model comparison by the difference in fit indices pValueNonNested for a nonnested model comparison by the difference in fit indices
SimResult的细节simResultpValueNested一个嵌套模型的比较拟合指数的差异pValueNonNested为一个nonnested的模型比较拟合指数的差异


实例----------Examples----------


## Not run: [＃不运行：]
library(lavaan)
loading <- matrix(0, 11, 3)
loading[1:3, 1] <- NA
loading[4:7, 2] <- NA
loading[8:11, 3] <- NA
path.A <- matrix(0, 3, 3)
path.A[2:3, 1] <- NA
path.A[3, 2] <- NA
param.A <- simParamSEM(LY=loading, BE=path.A)

model.A <- simModel(param.A, indLab=c(paste("x", 1:3, sep=""), paste("y", 1:8, sep="")))
out.A <- run(model.A, PoliticalDemocracy)

path.B <- matrix(0, 3, 3)
path.B[1:2, 3] <- NA
path.B[1, 2] <- NA
param.B <- simParamSEM(LY=loading, BE=path.B)

model.B <- simModel(param.B, indLab=c(paste("x", 1:3, sep=""), paste("y", 1:8, sep="")))
out.B <- run(model.B, PoliticalDemocracy)

u2 <- simUnif(-0.2, 0.2)
loading.mis <- matrix(NA, 11, 3)
loading.mis[is.na(loading)] <- 0
LY.mis <- simMatrix(loading.mis, "u2")
misspec <- simMisspecSEM(LY=LY.mis)

output.A.A <- runFit(model.A, PoliticalDemocracy, 5, misspec=misspec)
output.A.B <- runFit(model.A, PoliticalDemocracy, 5, misspec=misspec, analyzeModel=model.B)
output.B.A <- runFit(model.B, PoliticalDemocracy, 5, misspec=misspec, analyzeModel=model.A)
output.B.B <- runFit(model.B, PoliticalDemocracy, 5, misspec=misspec)

# The output may contain some warnings here. When the number of replications increases (e.g., 1000), the warnings should disappear.[输出可能包含一些警告。当复制数量增加时（例如，1000），警告消失。]
likRatioFit(out.A, out.B, output.A.A, output.A.B, output.B.A, output.B.B)

## End(Not run)[＃（不执行）]

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


注：
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