probe2WayMC(semTools)
probe2WayMC()所属R语言包：semTools
Probing two-way interaction on the residual-centered latent interaction
探索双向互动的残余中心的潜在作用
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Probing interaction for simple intercept and simple slope for the no-centered or mean-centered latent two-way interaction
探测交互简单的截距和斜率简单无中心的或潜在的平均值为中心的双向互动
用法----------Usage----------
probe2WayMC(fit, nameX, nameY, modVar, valProbe)
参数----------Arguments----------
参数：fit
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：nameX
The vector of the factor names used as the predictors. The first-order factor will be listed first. The last name must be the name representing the interaction term.
用作预测变量的因子名称的向量。一阶因素将被列在首位。最后的名称必须是代表的交互项的名称。
参数：nameY
The name of factor that is used as the dependent variable.
因子的名称是作为因变量。
参数：modVar
The name of factor that is used as a moderator. The effect of the other independent factor on each moderator variable value will be probed.
因子的名称，用作一个主持人。其他独立因素的影响，在每个调节变量值将被探测。
参数：valProbe
The values of the moderator that will be used to probe the effect of the other independent factor.
值的调节剂，将用于探测的其他独立的因素的影响。
Details
详细信息----------Details----------
Before using this function, researchers need to make the products of the indicators between the first-order factors using mean centering (Marsh, Wen, & Hau, 2004). Note that the double-mean centering may not be appropriate for probing interaction if researchers are interested in simple intercepts. The mean or double-mean centering can be done by the indProd function. The indicator products can be made for all possible combination or matched-pair approach (Marsh et al., 2004). Next, the hypothesized model with the regression with latent interaction will be used to fit all original indicators and the product terms. See the example for how to fit the product term below. Once the lavaan result is obtained, this function will be used to probe the interaction.
在使用此功能之前，研究人员需要使产品的指标之间的一阶因素意味着居中（沼泽，文厚，2004年）。需要注意的是双平均定心可能不适合探测互动，如果研究人员感兴趣的简单的拦截。 indProd函数可以通过平均或双均值定心。该指标的产品，可制成可用于所有可能的组合或对匹配的方法（Marsh等，2004年）。接着，与回归与潜交互虚拟模型将被使用，以适应所有的原始指标和产品条款。如何适应产品术语的例子。一旦lavaan得到的结果是，这个函数将被用于探测的相互作用。
Let that the latent interaction model regressing the dependent variable (Y) on the independent varaible (X) and the moderator (Z) be
让潜在的相互作用模型回归因变量（Y）和主持人（X）（Z）的独立varaible上
where b_0 is the estimated intercept or the expected value of Y when both X and Z are 0, b_1 is the effect of X when Z is 0, b_2 is the effect of Z when X is 0, b_3 is the interaction effect between X and Z, and r is the residual term.
b_0是Y都X和Z0b_1是X的效果估计的截距或预期值，当Z0b_2的效果是Z，当X0b_3是交互影响X和 X>和Z是剩余期限。
For probing two-way interaction, the simple intercept of the independent variable at each value of the moderator (Aiken & West, 1991; Cohen, Cohen, West, & Aiken, 2003; Preacher, Curran, & Bauer, 2006) can be obtained by
探测双向互动，简单的拦截的独立变量在主持人的每一个值（艾肯西，1991年，科恩，科恩，西，艾肯，2003年，传道，柯伦，与鲍尔，2006）可以通过以下方式获得
The simple slope of the independent varaible at each value of the moderator can be obtained by
简单的斜率独立varaible在每个值的调节剂可以通过以下方式获得
The variance of the simple intercept formula is
简单的截距公式的方差是
where Var denotes the variance of a parameter estimate and Cov denotes the covariance of two parameter estimates.
Var表示的参数估计的方差和Cov表示两个参数估计值的协方差。
The variance of the simple slope formula is
简单的斜率公式的方差是
Wald statistic is used for test statistic.
Wald统计量用于检验统计量。
值----------Value----------
A list with two elements:
有两个元素的列表：
SimpleIntercept The intercepts given each value of the moderator. This element will be shown only if the factor intercept is estimated (e.g., not fixed as 0).
SimpleIntercept，的拦截给每个值的主持人。此元素将显示，只有截距估计的因素（例如，固定为0）。
SimpleSlope The slopes given each value of the moderator.
SimpleSlope的斜坡，每个值的主持人。
In each element, the first column represents the values of the moderators specified in the valProbe argument. The second column is the simple intercept or simple slope. The third column is the standard error of the simple intercept or simple slope. The fourth column is the Wald (z) statistic. The fifth column is the p-value testing whether the simple intercepts or slopes are different from 0.
在每个元素中，第一列表示的主持人valProbe参数中指定的值。第二列是简单的拦截或简单的斜坡。第三列是简单的拦截或简单的斜率的标准误差。第四列是Wald（z）的统计。第五列是简单的截取或斜坡是否不同于0的p-值测试。
（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----------
indProd For creating the indicator products with no centering, mean centering, double-mean centering, or residual centering.
indProd创建指标的产品没有定心，意思是双中心，平均居中，或剩余定心。
probe3WayMC For probing the three-way latent interaction when the results are obtained from mean-centering, or double-mean centering.
probe3WayMC用于意味着定心，或双均值定心时，结果是从探测三通潜相互作用。
probe2WayRC For probing the two-way latent interaction when the results are obtained from residual-centering approach.
probe2WayRC用于探测从残余定心方法时的结果，得到的双向潜相互作用。
probe3WayRC For probing the two-way latent interaction when the results are obtained from residual-centering approach.
probe3WayRC用于探测从残余定心方法时的结果，得到的双向潜相互作用。
plotProbe Plot the simple intercepts and slopes of the latent interaction.
plotProbe绘制简单的截距与斜率的潜在作用。
实例----------Examples----------
library(lavaan)
dat2wayMC <- indProd(dat2way, 1:3, 4:6)
model1 <- "
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f12 =~ x1.x4 + x2.x5 + x3.x6
f3 =~ x7 + x8 + x9
f3 ~ f1 + f2 + f12
f12 ~~0*f1
f12 ~~ 0*f2
x1 ~ 0*1
x4 ~ 0*1
x1.x4 ~ 0*1
x7 ~ 0*1
f1 ~ NA*1
f2 ~ NA*1
f12 ~ NA*1
f3 ~ NA*1
"
fitMC2way <- sem(model1, data=dat2wayMC, meanstructure=TRUE, std.lv=FALSE)
summary(fitMC2way)
result2wayMC <- probe2WayMC(fitMC2way, c("f1", "f2", "f12"), "f3", "f2", c(-1, 0, 1))
result2wayMC
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 00:46:05
