pValueNested(simsem)
pValueNested()所属R语言包：simsem
Find p-values (1 - percentile) for a nested model comparison
的p值（1 - 百分位）的嵌套模型的比较
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 differences in fit indices between nested models with the simulation results of both parent and nested models when the nested model is true.
此功能将提供p值时，嵌套的模型是真实的父母和嵌套模型的模拟结果，与嵌套模型拟合指数之间的差异进行比较。
用法----------Usage----------
pValueNested(outNested, outParent, simNested, simParent, usedFit = NULL,
nVal = NULL, pmMCARval = NULL, pmMARval = NULL, df = 0)
参数----------Arguments----------
参数：outNested
SimModelOut that saves the analysis result of the nested model from the target dataset
SimModelOut，节省目标数据集的嵌套模型的分析结果
参数：outParent
SimModelOut that saves the analysis result of the parent model from the target dataset
SimModelOut，保存父模型的分析结果，从目标数据集
参数：simNested
SimResult that saves the analysis results of nested model from multiple replications
SimResult，节省了嵌套模型的分析结果，从多个复制
参数：simParent
SimResult that saves the analysis results of parent model from multiple replications
SimResult，节省了父模型的分析结果，从多个复制
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。
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 nested model (e.g., larger negative difference in CFI values or larger positive difference in RMSEA values) than the analysis result from the observed data.
在比较拟合指数，p值是提供偏好嵌套模型少比从所观察到的数据的分析结果（例如，在CFI的值或较大的正RMSEA值差异较大的负差值）的重复的数目的比例。
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 result. 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，从真实的数据与仿真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)
LY <- matrix(1, 4, 2)
LY[,2] <- 0:3
PS <- matrix(NA, 2, 2)
TY <- rep(0, 4)
AL <- rep(NA, 2)
TE <- diag(NA, 4)
linearModel <- simParamCFA(LY=LY, PS=PS, TY=TY, AL=AL, TE=TE)
LY2 <- matrix(1, 4, 2)
LY2[,2] <- c(0, NA, NA, 3)
unconstrainModel <- simParamCFA(LY=LY2, PS=PS, TY=TY, AL=AL, TE=TE)
nested <- simModel(linearModel, indLab=paste("t", 1:4, sep=""))
parent <- simModel(unconstrainModel, indLab=paste("t", 1:4, sep=""))
outNested <- run(nested, Demo.growth)
outParent <- run(parent, Demo.growth)
loadingMis <- matrix(0, 4, 2)
loadingMis[2:3, 2] <- NA
LYmis <- simMatrix(loadingMis, "runif(1, -0.1, 0.1)")
linearMis <- simMisspecCFA(LY=LYmis)
simNestedNested <- runFit(model=nested, data=Demo.growth, nRep=10, misspec=linearMis)
simNestedParent <- runFit(model=nested, data=Demo.growth, nRep=10, misspec=linearMis, analyzeModel=parent)
pValueNested(outNested, outParent, simNestedNested, simNestedParent)
## 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。
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发表于 2012-9-30 09:25:33
