miPowerFit(semTools)
miPowerFit()所属R语言包：semTools
Modification indices and their power approach for model fit evaluation
修正指标，它们的功率模型拟合评价方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The model fit evaluation approach using modification indices and their power proposed by Saris, Satorra, and van der Veld (2009, pp. 570-573).
该模型的拟合评价方法，修正指标和他们的权力提出，纱丽，Satorra，和van der草原（2009年，第570-573）。
用法----------Usage----------
miPowerFit(lavaanObj, stdLoad=0.4, cor=0.1, stdBeta=0.1, intcept=0.2, stdDelta=NULL, delta=NULL)
参数----------Arguments----------
参数：lavaanObj
The lavaan model object used to evaluate model fit
lavaan模型对象用于评估模型的拟合
参数：stdLoad
The amount of standardized factor loading that one would like to be detected (rejected). The default value is 0.4, which is suggested by Saris and colleagues (2009, p. 571).
的标准化因子负荷量，一个想被检测到（拒绝）。默认值是0.4，这是通过纱丽和他的同事（2009年，页571）。
参数：cor
The amount of factor or error correlations that one would like to be detected (rejected). The default value is 0.1, which is suggested by Saris and colleagues (2009, p. 571).
量的因素或错误的相关性，一个想被检测到（拒绝）。默认值是0.1，这是通过纱丽和他的同事（2009年，页571）。
参数：stdBeta
The amount of standardized regression coefficients that one would like to be detected (rejected). The default value is 0.1, which is suggested by Saris and colleagues (2009, p. 571).
标准化回归系数，想被检测到（拒绝）。默认值是0.1，这是通过纱丽和他的同事（2009年，页571）。
参数：intcept
The amount of standardized intercept (similar to Cohen's d that one would like to be detected (rejected). The default value is 0.2, which is equivalent to a low effect size proposed by Cohen (1988, 1992).
标准化的拦截量（类似科恩认为一个人想被检测到（拒绝）。默认值是0.2，这相当于一个低规模效应科恩（1988年，1992年）提出的。
参数：stdDelta
The vector of the standardized parameters that one would like to be detected (rejected). If this argument is specified, the value here will overwrite the other arguments above. The order of the vector must be the same as the row order from modification indices from the lavaan object. If a single value is specified, the value will be applied to all parameters.
标准化的参数，一个想被检测到（拒绝）的向量。如果指定此参数，这里的值将覆盖上述其他参数。向量的顺序作为修正指标从lavaan对象的行顺序必须是相同的。如果指定一个单一的值，该值将被应用到所有的参数。
参数：delta
The vector of the unstandardized parameters that one would like to be detected (rejected). If this argument is specified, the value here will overwrite the other arguments above. The order of the vector must be the same as the row order from modification indices from the lavaan object. If a single value is specified, the value will be applied to all parameters.
向量的非标准的参数，一个想被检测到（拒绝）。如果指定此参数，这里的值将覆盖上述其他参数。向量的顺序作为修正指标从lavaan对象的行顺序必须是相同的。如果指定一个单一的值，该值将被应用到所有的参数。
Details
详细信息----------Details----------
In the lavaan object, one can inspect the modification indices and expected parameter changes. Those values can be used to evaluate model fit by the method proposed by Saris and colleagues (2009). First, one should evaluate whether the modification index of each parameter is significant. Second, one should evaluate whether the power to detect a target expected parameter change is high enough. If the modification index is not significant and the power is high, there is no misspecification. If the modification index is significant and the power is low, the fixed parameter is misspecified. If the modification index is significant and the power is high, the expected parameter change is investigated. If the expected parameter change is large (greater than the the target expected parameter change), the parameter is misspecified. If the expected parameter change is low (lower than the target expected parameter change), the parameter is not misspecificied. If the modification index is not significant and the power is low, the decision is inconclusive.
在lavaan对象，可以检查修正指标和预期的参数变化。纱丽和他的同事（2009）提出的方法，这些值可以被用来评估模型的拟合。首先，应评估是否修改索引的每个参数的显着。其次，应评估是否足够高的预期目标探测参数的变化。如果修改索引是不显着的，功率高，有没有设定错误。如果修改指数是显着的，功率低，固定参数来年。如果变形指数是显着的功率是高的，预期的参数的变化进行了研究。如果期望的参数变化较大（大于预期目标参数的变化），该参数来年。如果预期的参数的变化是低（低于预期的目标的参数变化），该参数不在misspecificied。如果的修改索引是不显着的，功率低，决定是决定性的。
值----------Value----------
A data frame with these variables:
这些变量的数据框：
lhs The left-hand side variable (with respect to the lavaan operator)
左边刻度的左手侧变量（相对于到lavaan运算符）
op The lavaan syntax operator: "~~" represents covariance, "=~" represents factor loading, "~" represents regression, and "~1" represents intercept.
运lavaan语法运算符：“~~”代表协方差，“=”代表因子载荷，“~”表示回归，和“1”代表拦截。
rhs The right-hand side variable (with respect to the lavaan operator)
RHS的右手侧变量（相对于到lavaan运算符）
group The group of the parameter
本集团本集团的参数
mi The modification index of the fixed parameter
英里的固定参数的修改指数
epc The expected parameter change if the parameter is freely estimated
EPC的预期参数的变化，如果该参数自由估计
target.epc The target expected parameter change that represents the minimum size of misspecification that one would like to be detected by the test with a high power
target.epc预期目标的最小尺寸设定错误的人会喜欢被检测测试高功率的参数变化
std.epc The standardized expected parameter change if the parameter is freely estimated
std.epc标准化参数的变化，如果该参数自由估计
std.target.epc The standardized target expected parameter change
std.target.epc标准化的目标期望的参数变化
significant.mi Represents whether the modification index value is significant
significant.mi表示是否修改索引值是显着的
high.power Represents whether the power is enough to detect the target expected parameter change
high.power代表电源是否足以探测到预期目标参数的变化
decision The decision whether the parameter is misspecified or not: "M" represents the parameter is misspecified, "NM" represents the parameter is not misspecified, "EPC:M" represents the parameter is misspecified decided by checking the expected parameter change value, "EPC:NM" represents the parameter is not misspecified decided by checking the expected parameter change value, and "I" represents the decision is inconclusive.
参数是否是来年或决定的决定："M"表示该参数来年，"NM"表示该参数不来年，"EPC:M"代表的决定检查期望的参数，参数来年变化值，"EPC:NM"表示参数来年决定通过检查期望的参数变化值，和"I"的决定是不确定的。
（作者）----------Author(s)----------
Sunthud Pornprasertmanit (University of Kansas; <a href="mailto:psunthud@ku.edu">psunthud@ku.edu</a>)
参考文献----------References----------
参见----------See Also----------
moreFitIndices For the additional fit indices information
moreFitIndices对于其他拟合指数信息
实例----------Examples----------
library(lavaan)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HolzingerSwineford1939, group="sex", meanstructure=TRUE)
miPowerFit(fit)
model <- '
# latent variable definitions[潜变量的定义]
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + a*y2 + b*y3 + c*y4
dem65 =~ y5 + a*y6 + b*y7 + c*y8
# regressions[回归]
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual correlations[残留的相关性]
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit2 <- sem(model, data=PoliticalDemocracy, meanstructure=TRUE)
miPowerFit(fit2, stdLoad=0.3, cor=0.2, stdBeta=0.2, intcept=0.5)
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 00:45:21
