pValueNonNested(simsem)
pValueNonNested()所属R语言包：simsem
Find p-values (1 - percentile) for a non-nested model comparison
的p值（1 - 百分位）的非嵌套模型的比较
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function will provide p value from comparing the results of fitting real data into two models against the simulation from fitting the simulated data from both models into both models. The p values from both sampling distribution under the datasets from the first and the second models are reported.
此功能将提供比较两个模型的拟合真实的数据对模拟的结果从两个模型的模拟数据拟合到这两个模型的p值。报告从两个采样从所述第一和所述第二模式下的数据集的分布的p值。
用法----------Usage----------
pValueNonNested(outMod1, outMod2, dat1Mod1, dat1Mod2, dat2Mod1, dat2Mod2,
usedFit = NULL, nVal = NULL, pmMCARval = NULL, pmMARval = NULL, df = 0,
onetailed=FALSE)
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：nVal
The sample size value that researchers wish to find the p value from.
样本大小，研究人员希望找到p值的值。
参数：pmMCARval
The percent missing completely at random value that researchers wish to find the p value from.
％完全丢失，研究人员希望找到p值随机值。
参数：pmMARval
The percent missing at random value that researchers wish to find the the p value from.
失踪的随机值，研究人员希望找到的p值的百分比。
参数：df
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.
预测的拟合指数的预测中使用的样条方法的自由度。如果df是0，样条方法将不适用。
参数：onetailed
If TRUE, the function will convert the p value based on two-tailed test.
如果TRUE，该函数将转换基于双尾检验的p值。
Details
详细信息----------Details----------
In comparing fit indices, the p value is the proportion of the number of replications that provide less preference for either model 1 or model 2 than the analysis result from the observed data. In two-tailed test, the function will report the proportion of values under the sampling distribution that are more extreme that one obtained from real data. If the resulting p value is high (> .05) on one model and low (< .05) in the other model, the model with high p value is preferred. If the p values are both high or both low, the decision is undetermined.
在比较拟合指数，p值是复制提供以下为模式1或模式2的偏好比从所观察到的数据的分析结果的数量的比例。在双尾检验中，该函数将报告的比例值下的抽样分布，更极端的是，获得真实的数据。如果结果p值高（> 0.05）的一个模型，低（<0.05），在其他的模型，该模型高p这个值是首选。 ，如果p值都高或都为低，这一决定是不确定的。
值----------Value----------
This function provides a vector of p values based on the comparison of the difference in fit indices from the real data with the simulation results. The p values of fit indices are provided, as well as two additional values: andRule and orRule. The andRule is based on the principle that the model is retained only when all fit indices provide good fit. The proportion is calculated from the number of replications that have all fit indices indicating a better model than the observed data. The proportion from the andRule is the most stringent rule in retaining a hypothesized model. The orRule is based on the principle that the model is retained only when at least one fit index provides good fit. The proportion is calculated from the number of replications that have at least one fit index indicating a better model than the observed data. The proportion from the orRule is the most lenient rule in retaining a hypothesized model.
此功能提供一个矢量拟合指数的差异，从真实的数据与模拟结果的比较的基础上的p值。 P值的拟合指数，以及另外两个值：andRule和orRule。 andRule的基础上，该模型时，只保留了所有的拟合指数提供适合的原则。有一个更好的模型比观测到的数据拟合指数的复制数的比例计算。的比例从andRule保留一个假设的模型是最严格的规则。 orRule的基础上，该模型保留，只有当至少有一个合适的索引提供适合的原则。至少有一个合适的指标一个更好的模型比观测到的数据复制数的比例计算。的比例从orRule保留一个假设的模型是最宽松的规则。
（作者）----------Author(s)----------
Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)
参见----------See Also----------
SimModelOut to see how to get the analysis result of observed data
SimModelOut来看看如何得到观测数据的分析结果
SimResult to run a simulation study
SimResult运行的模拟研究
runFit to run a simulation study based on the parameter estimates from the analysis result of observed data
runFit运行参数的模拟研究的基础上估计，从观测数据的分析结果
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。]
pValueNonNested(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：
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 09:25:39
