我的SEM模型似乎还不错，但是当试图让组适合进行多组分析时，R会大喊再次警告。
这是我的模型（数据将在后面显示）：
model <- '
sndPre =~ R + int + rldct
dtls ~ d*open + e*supp
open ~ c*gta
gta ~ b*sndPre
sndPre ~ a*supp + age
open ~~ gta
R ~~ dtls
R ~~ int
int ~~ dtls
indirect := a*b*c*d
direct := e
total := e + (a*b*c*d)
'
运行它可以很好地适应一切：
summary(fit <- sem(model, data = df), fit.measures = T)
但是适合“免费”或“受限”模型以应用多组分析：
summary(fit.mg.gdr <- sem(model, data = df,
group = "gender"),
fit.measures = T)
summary(fit.mg.gdr.cnst <- sem(model,
data = df,
group = "gender",
group.equal = c("intercepts",
"regressions")),
fit.measures = T)
产生警告：
lavaan警告：优化器警告未找到解决方案！
按组拟合type而不是fmly时，也会发生同样的情况。
如果有人可以帮助我，我将不胜感激。
乌里
这是我的数据
df <- structure(list(supp = c(-1.26105699321048, 1.23539204000555,
0.998851022079735, -0.190921681071981, -1.28050150562454, 1.88566871428553,
-0.346185741866873, 2.22353594331693, -0.899501224645076, 0.456412565066874,
-1.04706110379691, -0.921732740299538, -0.0748627524431053, -0.936373552588429,
1.34849384296525, 2.00592623479799, 0.541045414056009, -0.793261814499457,
-0.340594037718542, 0.0501766033631645, -0.133884381988423, 1.25734187466122,
-0.118313651917272, -0.376004851315653, -0.578002604556701, 0.61490135465745,
-0.731484429737429, -0.443541245848682, 1.127203874985, -1.94705304738397,
-0.262864086600616, 0.836658140574031, 0.932007060340215, 0.462261173870058,
0.842275379483654, 0.909358352545815, -1.57531188615793, -0.44738703049337,
0.0313710067871067, -0.83968986365572, -0.82420486119427, 1.37752074578506,
0.808173067751332, -0.459141855711599, -0.143956128586532, -2.0662260120717,
-0.94160943714496, -1.33187171317916, 1.090350360849, -0.682954192050242,
0.710637734125368, 1.65867082804067, 0.325637630371078, -0.611847463704592,
-1.60568458666961, -0.355403157908715, -0.373464430910395, -0.676545468797976,
0.740129996103287, -1.20452185918761, 1.09947403532858, -0.129112612454232,
-1.91125544865754, -1.0797149516082, 0.575532093748616, -0.621895872474711,
-0.215693078909198, 0.141524098826785, -0.492789877905876, -0.508654550919966,
-1.155140140116, 0.383157434711058, 0.420161412281436, -1.40552653940339,
1.31607100170934, 1.05443856378575, -2.04814817005446, 1.05443856378575,
0.117203553157344, -0.799247977488777, -0.071283009062627, 1.42448397835845,
0.043033574527453, 0.989878845252324, 1.00469999108005, 0.321136912972101,
1.09523404904648, -0.544087482645234, 1.10318971667871, 1.27607992892502,
1.45254026399367, -1.44247186686377, 1.18575360443271, -0.498083362700643,
0.111118998026078, -0.474752580097199, 0.608318711719373, -1.02659137731459
), rldct = c(0.245353729370027, -0.308948664922806, -1.52257018899952,
-0.0622393093563785, -0.308948664922806, -0.0363613989903612,
-0.0737075536591954, 0.0769747339892045, -1.52257018899952, 0.0345745707698335,
0.45304032418845, -0.754467922085042, -0.984915246362911, 1.51806368492016,
-1.52257018899952, -1.52257018899952, 1.05685750405161, 0.364414652288875,
1.63017529926735, 1.22387411707763, -0.179234164546305, 0.932198355591301,
-1.52257018899952, -0.308948664922806, 1.31556690619222, 0.45304032418845,
-1.52257018899952, -0.209305380528335, 2.1704980278988, 0.748880570563087,
-0.615704912542176, 0.0277244738595314, -0.473637451978715, -1.10902161083733,
-0.0555167627056605, -1.52257018899952, 0.932198355591301, 0.550319749201526,
0.874213648556419, -1.52257018899952, -1.21539315694016, 0.45304032418845,
0.245353729370027, -1.52257018899952, -0.439719946239881, -0.11477704827939,
0.45304032418845, 0.45304032418845, 1.05685750405161, -0.415865290238098,
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2.1704980278988, 0.620619013038226, 0.576140282545116, 1.05685750405161,
2.1704980278988, 0.309589702123866, -0.179234164546305, 1.38835015610913,
0.521570543504756, -0.364864803986247, -1.52257018899952, -0.415865290238098,
1.34747237595098, -1.03695973787835, 1.70944951184896, 1.26898550689769,
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-0.505498127259985, -0.505498127259985, 0.219174133457963, 0.45304032418845
), R = c(-1.62786138011885, -0.666153591682604, 1.18212856421831,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 0)), row.names = c(NA, -98L), class = "data.frame")
答案 0 :(得分：0)
感谢您发布数据，但是您没有提供model.mg对象，因此这仍然不是可重复的示例。我会说SEM是基于渐近理论的，而 N = 98已经是一个小样本。将其分为50组和48组使其更不合适，并且更有可能遇到收敛问题（例如，找不到解决方案，无法计算 SE 或Heywood案例的解决方案）。
考虑到您的小 N ，我会考虑采用单组方法进行中度调解。
https://doi.org/10.1080/00273170701341316
您可以在lavaan的Google论坛上搜索“仲裁中介”语法示例。