Lavann 模型估计失败

问题描述 投票:0回答:1

所以我今天开始使用 lavaan 和 sem() 。与几个构造/潜在变量和一个回归模型配合得很好。然而,lavaan 现在失败了。即使当我尝试从回归模型中删除新变量时,我仍然收到此错误:

'''

    Warning message:
    lavaan->lav_lavaan_step11_estoptim():  
    Model estimation FAILED! Returning starting values

'''

这可能是什么原因造成的? 输出结果:

dput(head(data2))
structure(list(everlied = c(0, 0, 0, 0, 0, 0), Buswebsite = c(3, 
1, 4, 1, 3, 3), BusFacebookpage = c(3, 2, 4, 2, 3, 3), 
periodicals = c(3, 2, 4, 3, 3, 3), internetsearch = c(3, 4, 4, 2, 
4, 4), googlebuslisting = c(3, 5, 5, 3, 3, 3), 
internetdiscussforum = c(3, 3, 4, 4, 3, 4), consumerratingssite = 
c(3,4, 5, 2, 4, 4), friends = c(5, 4, 5, 3, 4, 5), family = c(5, 
5, 5, NA, 4, 5), doctors = c(4, 4, 5, 3, 4, 4), politicians = 
c(3,1, 1, 3, 1, 1), salespeople = c(3, 2, 1, 3, 2, 1), like = 
c(3, 2, 2, 3, 2, 1), newimmigrants = c(3, 3, 2, 2, 3, 1),
businessowners = c(3, 2, 2, 3, 3, 1), celebrities = c(3, 3, 1, 2, 
3, 1), commercialactors = c(3, 2, 1, 2, 3, 1), religious = c(4,
4, 4, 2, 4, 1), socmediainfluencers = c(3, 3, 1, 4, 3, 1), 
teachers = c(4, 4, 4, 3, 4, 1), gender = c(0, 0, 0, 1, 0, 0), age 
= c(5, 5, 3, 3, 3, 2), education = c(4, 2, 3, 4, 5, 3), income = 
c(3, 2, 2, 2, 3, 4), region = c(3, 3, 7, 7, 3, 7)), row.names = 
c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"))

我的模型非常驯服。观察到的结果变量是有序的(它是李克特量表值)。人口统计的预测变量是分类的。其余变量也是有序的。我第一次尝试时,我只使用了前两个潜在变量。巧合的是,我本身找不到任何有关此错误的信息,但找到了一篇提到方差的帖子。我确实尝试插入 auto.var = TRUE 但这没有帮助。

这就是我所拥有的。

`
    model <- '
    + person  =~ family + friends
    + demographic =~ age + gender + income + education + 
    region 
    + instit =~ doctors+politicians+religious+businessowners
    + soc =~ salespeople + celebrities + commercialactors + 
    socmediainfluencers + newimmigrants
    + info =~ Buswebsite + BusFacebookpage + periodicals + 
    internetsearch + googlebuslisting + internetdiscussforum + 
    consumerratingssite
    + like ~ person + demographic + instit + soc + info 
+ '
> model_out <- sem(model, data=dat2)

谢谢你

r r-lavaan sem
1个回答
0
投票

所以,在尝试了几次之后,我决定巩固一些潜在变量。看来 lavaan/sem 不喜欢超过 4 个结构。与 4 个潜在变量完美配合。

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