probe3WayRC(semTools)
probe3WayRC()所属R语言包：semTools
Probing three-way interaction on the residual-centered latent interaction
探索三方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 three-way interaction (Pornprasertmanit, Schoemann, Geldhof, & Little, submitted)
简单的拦截和简单的斜率探测互动的剩余为中心的潜三双向互动（Pornprasertmanit，Schoemann，Geldhof，与小提交）
用法----------Usage----------
probe3WayRC(fit, nameX, nameY, modVar, valProbe1, valProbe2)
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 three first-order factors will be listed first. Then the second-order factors will be listeed. The last element of the name will represent the three-way interaction. Note that the fourth element must be the interaction between the first and the second variables. The fifth element must be the interaction between the first and the third variables. The sixth element must be the interaction between the second and the third variables.
用作预测变量的因子名称的向量。三个一阶因素将被列在首位。然后，的二阶因素将listeed，。的最后一个元素的名称将代表三方互动。请注意，所述第四元件必须是所述第一和所述第二变量之间的相互作用。所述第五元件必须是在第一和第三个变量之间的相互作用。所述第六元件必须是第二和第三变量之间的相互作用。
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 two factors that are used as the moderators. The effect of the independent factor on each combination of the moderator variable values will be probed.
两个因素作为版主的名称。独立因素对主持人变量值的每一种组合的效果将被探测。
参数：valProbe1
The values of the first moderator that will be used to probe the effect of the independent factor.
的值，将用于探测的独立危险因素的影响的第一主持人。
参数：valProbe2
The values of the second moderator that will be used to probe the effect of the independent factor.
所述第二慢的值，将用于探测的独立危险因素的影响。
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 (Geldhof, Pornprasertmanit, Schoemann, & Little, in press). 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。接下来，假设模型的回归与潜在的互动，以适应所有的原指标和产品的条款（Geldhof，Pornprasertmanit，Schoemann，与小，出版中）。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 probe3WayMC 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（参数估计和他们之间的协方差矩阵参数估计）计算，请参阅“probe3WayMC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 first moderator specified in the valProbe1 argument. The second column represents the values of the second moderator specified in the valProbe2 argument. The third column is the simple intercept or simple slope. The fourth column is the standard error of the simple intercept or simple slope. The fifth column is the Wald (z) statistic. The sixth column is the p-value testing whether the simple intercepts or slopes are different from 0.
在每个元件中，第一列表示的值的第1valProbe1参数中指定的调节剂。第二列表示的值valProbe2参数中指定的第二主持人。第三列是简单的拦截或简单的斜坡。第四列是简单的拦截或简单的斜率的标准误差。第五列是Wald（z）的统计。第六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。
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。
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)
dat3wayRC <- orthogonalize(dat3way, 1:3, 4:6, 7:9)
model3 <- "
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3 =~ x7 + x8 + x9
f12 =~ x1.x4 + x2.x5 + x3.x6
f13 =~ x1.x7 + x2.x8 + x3.x9
f23 =~ x4.x7 + x5.x8 + x6.x9
f123 =~ x1.x4.x7 + x2.x5.x8 + x3.x6.x9
f4 =~ x10 + x11 + x12
f4 ~ f1 + f2 + f3 + f12 + f13 + f23 + f123
f1 ~~ 0*f12
f1 ~~ 0*f13
f1 ~~ 0*f123
f2 ~~ 0*f12
f2 ~~ 0*f23
f2 ~~ 0*f123
f3 ~~ 0*f13
f3 ~~ 0*f23
f3 ~~ 0*f123
f12 ~~ 0*f123
f13 ~~ 0*f123
f23 ~~ 0*f123
x1 ~ 0*1
x4 ~ 0*1
x7 ~ 0*1
x10 ~ 0*1
x1.x4 ~ 0*1
x1.x7 ~ 0*1
x4.x7 ~ 0*1
x1.x4.x7 ~ 0*1
f1 ~ NA*1
f2 ~ NA*1
f3 ~ NA*1
f12 ~ NA*1
f13 ~ NA*1
f23 ~ NA*1
f123 ~ NA*1
f4 ~ NA*1
"
fitRC3way <- sem(model3, data=dat3wayRC, meanstructure=TRUE, std.lv=FALSE)
summary(fitRC3way)
result3wayRC <- probe3WayRC(fitRC3way, c("f1", "f2", "f3", "f12", "f13", "f23", "f123"), "f4", c("f1", "f2"), c(-1, 0, 1), c(-1, 0, 1))
result3wayRC
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 00:46:26
