probe2WayRC(semTools)
probe2WayRC()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 residual-centered latent two-way interaction (Pornprasertmanit, Schoemann, Geldhof, & Little, submitted)
探测潜在的剩余为中心的双向互动（Pornprasertmanit，Schoemann，Geldhof，小提交简单的截距和斜率简单的互动）
用法----------Usage----------
probe2WayRC(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 and residualize the products by the original indicators (Lance, 1988; Little, Bovaird, & Widaman, 2006). The process can be automated by the indProd function. Note that 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，研究人员需要的原始指标（1988年，阿姆斯特朗小，Bovaird，与Widaman，2006年），使产品的各项指标之间的一阶因素和residualize的产品。这个过程可以实现自动化的indProd功能。请注意，可以针对所有可能的组合或对匹配的方法（Marsh等，2004年）的指示器产品。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。
The probing process on residual-centered latent interaction is based on transforming the residual-centered result into the no-centered result. See Pornprasertmanit, Schoemann, Geldhof, and Little (submitted) for further details. Note that this approach based on a strong assumption that the first-order latent variables are normally distributed. The probing process is applied after the no-centered result (parameter estimates and their covariance matrix among parameter estimates) has been computed. See the probe2WayMC for further details.
探测过程中剩余为中心的潜互动的基础上改造的剩余为中心的结果到无中心的结果。的进一步详情，请参阅Pornprasertmanit，Schoemann，Geldhof，小（提交）。注意，此方法基于一个很强的假设，一阶潜变量是正态分布的。探测过程中应用后无中心的结果（参数估计和参数估计值之间的协方差矩阵）计算。请参阅probe2WayMC进一步详情。
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。
probe2WayMC For probing the two-way latent interaction when the results are obtained from mean-centering, or double-mean centering.
probe2WayMC用于探测的双向潜相互作用时，结果是从平均定心，或双均值定心。
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。
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)
dat2wayRC <- orthogonalize(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
"
fitRC2way <- sem(model1, data=dat2wayRC, meanstructure=TRUE, std.lv=FALSE)
summary(fitRC2way)
result2wayRC <- probe2WayRC(fitRC2way, c("f1", "f2", "f12"), "f3", "f2", c(-1, 0, 1))
result2wayRC
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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 00:46:12
