probe3WayMC(semTools)
probe3WayMC()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----------
probe3WayMC(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 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 two moderators (Z and W) be
让潜在的相互作用模型回归因变量（Y）的的独立varaible（X）和两个主持人（Z和W）是
where b_0 is the estimated intercept or the expected value of Y when X, Z, and W are 0, b_1 is the effect of X when Z and W are 0, b_2 is the effect of Z when X and W is 0, b_3 is the effect of W when X and Z are 0, b_4 is the interaction effect between X and Z when W is 0, b_5 is the interaction effect between X and W when Z is 0, b_6 is the interaction effect between Z and W when X is 0, b_7 is the three-way interaction effect between X, Z, and W, and r is the residual term.
b_0的估计拦截的预期值Y当X，Z，W0b_1是效果XZ和W0b_2的效果是ZX和W是0， b_3的效果WX和Z0b_4是在X和Z之间的互作效应当W0b_5是X和WZ0b_6之间的互作效应之间的互作效应Z和W如果X0“b_7是三方互动的效果，X，Z，和W的和r的剩余期限。
For probing three-way interaction, the simple intercept of the independent variable at the specific values of the moderators (Aiken & West, 1991) can be obtained by
探测三方互动，简单的拦截独立变量的主持人（艾肯西，1991年）的具体数值可以通过以下方式获得：
The simple slope of the independent varaible at the specific values of the moderators can be obtained by
简单的斜率独立varaible在主持人的具体值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 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。
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)
dat3wayMC <- indProd(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
"
fitMC3way <- sem(model3, data=dat3wayMC, meanstructure=TRUE, std.lv=FALSE)
summary(fitMC3way)
result3wayMC <- probe3WayMC(fitMC3way, c("f1", "f2", "f3", "f12", "f13", "f23", "f123"), "f4", c("f1", "f2"), c(-1, 0, 1), c(-1, 0, 1))
result3wayMC
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 00:46:19
