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我有一个数据集，其中一些（但不是所有）受试者在时间 2 参与。我想跨时间和跨组运行测量不变性分析。当然，对于那些只在时间 1 参与的人，不会为他们估计时间 2 的潜在变量。

lavaan您可以在模型中为每个变量指定不同的权重，方法是在其前面加上权重向量。例如，c(0.5, 0.2) 将赋予第一组 0.5 的权重，赋予第二组 0.2 的权重。对我来说，问题是我无法对潜在变量执行此操作。我的问题：我如何告诉lavaan只为一组而不是另一组估计潜在变量？

示例代码：

model <- '
# Measurement model ---
factor1_time1 =~ 1*var1_t1 + var2_t1 + var3_t1
factor2_time1 =~ 1*var4_t1 + var5_t1 + var6_t1
factor3_time1 =~ 1*var7_t1 + var9_t1 + var9_t1

# !! Note: I only want this to apply to one of the groups !!
factor1_time2 =~ 1*var1_t2 + var2_t2 + var3_t2
factor2_time2 =~ 1*var4_t2 + var5_t2 + var6_t2
factor3_time2 =~ 1*var7_t2 + var9_t2 + var9_t2

# Factor Covariances ---

# all time 1 factors with each other
factor1_time1 ~~ factor2_time1
factor1_time1 ~~ factor3_time1
factor2_time1 ~~ factor3_time1

# all time 2 factors with each other
factor1_time2 ~~ factor2_time2
factor1_time2 ~~ factor3_time2
factor2_time2 ~~ factor3_time2

# factor1_time1 with all Time 2 factors
factor1_time1 ~~ factor1_time2
factor1_time1 ~~ factor2_time2
factor1_time1 ~~ factor3_time2

# factor2_time1 with all Time 2 factors
factor2_time1 ~~ factor1_time2
factor2_time1 ~~ factor2_time2
factor2_time1 ~~ factor3_time2

# factor3_time1 with all Time 2 factors
factor3_time1 ~~ factor1_time2
factor3_time1 ~~ factor2_time2
factor3_time1 ~~ factor3_time2

# Lag item residuals ---
#factor1 items
var1_t1 ~~ var1_t2
var2_t1 ~~ var2_t2
var3_t1 ~~ var3_t2

#factor2 items
var4_t1 ~~ var4_t2
var5_t1 ~~ var5_t2
var6_t1 ~~ var6_t2

#factor 3 times
var7_t1 ~~ var7_t2
var8_t1 ~~ var8_t2
var9_t1 ~~ var9_t2
'
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1 回答 1

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（此答案归功于阿姆斯特丹大学的 Jorgensen 博士）

library(lavaan)

HS.model <- '
group: 1
visual  =~ x1 + a*x2 + b*x3
textual =~ x4 + x5 + x6

group: 2
visual  =~ x1 + a*x2 + b*x3
speed   =~ x7 + x8 + x9
'

fit <- cfa(HS.model, data=HolzingerSwineford1939, group="school")
于 2017-08-22T22:23:43.090 回答