r - sem.mi 或 runMI

Question

我正在 lavaan 中（使用序数）进行路径分析，并希望使用估算数据。

但是，无论我是单独估算数据并使用 runMI 还是让原始数据作为 sem.mi 命令的一部分，我都会得到相同的错误：

Error: evaluation nested too deeply: infinite recursion / options(expressions=)?
Error during wrapup: evaluation nested too deeply: infinite recursion / options(expressions=)?

如果我运行：options(expressions = 100000) 错误消息更改为：Error:protect(): protection stack overflow

我试图改变

--max-ppsize=500000 

但是在命令行中我无法访问 rstudio.exe （说：系统找不到指定的路径， - 即使我仔细检查了路径：

C:\Program Files\RStudio\bin\rstudio.exe --max-ppsize=500000)

我可以做些什么来使用推算数据运行我的分析或将其推算为路径分析估计的一部分？

这是我的代码：

imp <- mice(dat2,m=5,print=F)
imputedData <- NULL
for(i in 1:5) {
  imputedData[[i]] <- complete(x=imp, action=i, include=FALSE) 
}
model5 <- 'ceadiff ~ mompa + cdpea + momabhx
mompa ~ b1*peadiff + c*momabhx + cdpea + b2*mommhpsi
peadiff ~ a1*momabhx + mommhpsi
cdpea ~ momabhx + mommhpsi
mommhpsi ~ a2*momabhx
peadiff ~~ cdpea
direct := c
indirect1 := a1 * b1
indirect1 := a2 * b2
total    := c + (a1 * b1) + (a2 * b2)'

fit5 <- runMI(model5, data = imputedData, fun="sem", ordered = "mompa")
summary(fit5, standardized = TRUE, fit = TRUE, ci = T)

# or: 

fit5 <- sem.mi(model5, data = dat2, m=5, ordered = "mompa")
summary(fit5, standardized = TRUE, fit = TRUE, ci = T)

PS 在这种情况下它会打印带有警告的摘要，但不会打印 p 值或 CI，因此我无法确定哪些系数是 sig。：

fit5 <- sem.mi(model5, data = dat2, m=5, ordered = "mompa")
summary(fit5)

** WARNING ** lavaan (0.5-23.1097) model has NOT been fitted
** WARNING ** Estimates below are simply the starting values

谢谢！

PS我不知道如何提供我的数据样本。

这是未经估算的数据输出：

    > dput(dat2)
structure(list(id = structure(c(145, 253, 189, 305, 149, 567, 
151, 853, 272, 67, 111, 695, 1695, 1301, 2322, 1335, 1490, 580, 
209, 1109, 1317, 812, 1459, 2150, 685, 1583, 839, 2156, 1627, 
1103, 649, 2294, 1712, 1711, 793, 1425, 1114, 146, 1529, 985, 
1889, 1974, 444, 1664, 1569, 859, 1947, 1219, 1427, 1533, 2143, 
769, 256, 147, 1393, 1847, 1967, 1651, 1084, 1343, 996, 1765, 
1596, 2157, 978, 1448, 915, 1411, 1412, 675, 1876, 53, 400, 2103, 
1028, 663, 1090, 360, 2134, 1937, 1061, 1823, 935, 891, 1968, 
34, 487, 207, 295, 1118, 1164, 1053, 1511, 777, 1760, 38, 480, 
459, 307, 1962, 199, 499, 1375, 782, 1855, 1624, 109, 1481, 483, 
536, 972, 1151, 19, 403, 543, 502, 2251, 254, 429, 2118, 1272, 
1995, 982, 1748, 1641, 1994, 1718, 510, 494, 273, 602, 549, 293, 
1796, 1497, 1197, 1874, 1179, 159, 205, 242, 299, 100, 1200, 
579, 870, 1482, 2131, 33, 1319, 148, 1297, 626, 1051, 1948, 1057, 
1581, 1349, 1284, 1178, 1178, 1044, 1001, 547, 276, 507, 871, 
698, 1006, 1946, 2101, 68, 265, 1186, 1895, 1864, 1884, 1553, 
1761, 2171, 168, 30, 1132, 1983, 1897, 1383, 1353, 1697, 1752, 
505, 1605, 1144, 1358, 1052, 1645, 1346, 14, 439, 2154, 932, 
971, 2104, 1345, 1821, 52, 1642, 1661, 1835, 1232, 2132, 809, 
606, 54, 528, 59, 1848, 232, 1750, 2340, 882, 716, 2105, 711, 
2109, 2353, 41, 2144, 552, 304, 2404, 1527, 1980, 927, 1586, 
1805, 1982, 1181, 2163, 861, 198, 1404, 986, 1404, 238, 2115, 
1125), format.spss = "F4.0", display_width = 11L), peadiff = structure(c(4, 
7, 2, 2, 3, 4, 5, 5, 2, 6, 2, 6, 4, 3, 4, 5, 2, 3, 2, 1, 1, 3, 
3, 3, 3, 5, 6, 3, 2, 2, 2, 4, 2, 2, 3, 5, 2, 4, 6, 2, 2, 3, 2, 
1, 7, 7, 2, 5, 6, 4, 4, 4, 2, 9, 3, 4, 6, 7, 3, 3, 4, 3, 7, 5, 
7, 4, 1, 1, 6, 14, 6, 2, 4, 3, 6, 4, 6, 7, 8, 5, 3, 4, 5, 1, 
5, 4, 4, 9, 6, 3, 4, 3, 6, 6, 3, 1, 2, 2, 5, 4, 4, 1, 1, 3, 3, 
3, 3, 7, 5, 4, 3, 4, 3, 4, 3, 4, 4, 4, 6, 3, 1, 1, 6, 4, 6, 9, 
2, 3, 3, 7, 4, 1, 2, 9, 2, 3, 6, 1, 5, 3, 8, 4, 0, 4, 4, 6, 2, 
4, 2, 7, 6, 8, 5, 3, 10, 3, 1, 4, 6, 6, 6, 5, 4, 5, 3, 7, 3, 
4, 8, 4, 7, 4, 15, 4, 0, 2, 5, 3, 3, 3, 5, 7, 4, 7, 5, 2, 3, 
2, 8, 5, 2, 5, 4, 5, 2, 4, 3, 3, 5, 4, 4, 3, 5, 2, 4, 3, 2, 1, 
6, 2, 8, 2, 6, 3, 0, NA, 6, 3, 4, 2, 9, 3, 4, 4, 2, 12, 5, 4, 
0, 2, 2, 5, 2, 1, 3, 3, 4, 3, 2, 4, 7, 9, 5, 4, 6, 8), format.spss = "F8.2", display_width = 10L), 
    ceadiff = structure(c(5, 4, 2, 1, 2, 2, 3, 4, 3, 4, 0, 2, 
    2, 1, 4, 2, 6, 4, 2, 2, 2, 3, 4, 2, 6, 4, 4, 4, 5, 3, 2, 
    4, 4, 3, 1, 7, 3, 6, 8, 2, 3, 2, 2, 1, 4, 5, 0, 4, 2, 3, 
    4, 4, 1, 5, 3, 1, 4, 3, 5, 2, 0, 4, 0, 5, 4, 2, 4, 3, 2, 
    7, 7, 0, 5, 0, 4, 5, 2, 4, 4, 3, 2, 4, 2, 2, 3, 4, 4, 3, 
    1, 3, 4, 6, 8, 2, 2, 5, 2, 6, 6, 2, 4, 0, 2, 4, 2, 2, 2, 
    5, 2, 2, 7, 6, 3, 6, 4, 8, 2, 2, 5, 1, 1, 1, 2, 1, 3, 3, 
    4, 3, 5, 8, 2, 1, 4, 3, 1, 3, 5, 5, 2, 4, 4, 5, 1, 1, 8, 
    6, 1, 4, 12, 5, 7, 8, 3, 6, 5, 6, 3, 5, 4, 3, 3, 4, 6, 4, 
    2, 6, 2, 3, 4, 2, 7, 4, 7, 4, 3, 0, 3, 0, 2, 2, 1, 3, 5, 
    1, 4, 2, 1, 2, 7, 4, 4, 4, 8, 6, 2, 6, 1, 1, 5, 3, 0, 5, 
    8, 4, 8, 3, 0, 3, 4, 5, 5, 2, 6, 0, 6, NA, 4, 4, 1, 3, 12, 
    2, 0, 4, 0, 5, 4, 3, 2, 1, 1, 5, 5, 6, 3, 1, 2, 1, 4, 2, 
    8, 6, 3, 0, 1, 3), format.spss = "F8.2", display_width = 10L), 
    cdpea = structure(c(22, 18, 17, 13, 19, 20, 19, 20, 17, 17, 
    17, 14, 17, 15, 21, 12, 16, 15, 14, 17, 19, 18, 17, 18, 19, 
    16, 18, 15, 16, 18, 17, 19, 18, 15, 16, 18, 18, 17, 22, 18, 
    18, 12, 19, 16, 15, 17, 14, 17, 15, 19, 17, 18, 14, 17, 19, 
    20, 16, 6, 12, 17, 17, 16, 13, 20, 18, 16, 16, 18, 21, 17, 
    21, 13, 17, 14, 18, 15, 18, 17, 23, 19, 17, 18, 15, 17, 19, 
    15, 21, 17, 20, 16, 15, 18, 15, 18, 17, 18, 16, 18, 21, 16, 
    19, 21, 18, 16, 19, 18, 18, 18, 18, 18, 19, 20, 20, 22, 14, 
    19, 18, 16, 22, 14, 16, 17, 18, 15, 16, 19, 16, 19, 18, 18, 
    15, 18, 19, 16, 16, 18, 15, 13, 12, 20, 19, 18, 19, 13, 19, 
    19, 16, 20, 18, 18, 18, 18, 18, 18, 19, 15, 14, 18, 16, 15, 
    15, 18, 18, 18, 18, 20, 17, 16, 19, 18, 19, 17, 18, 18, 16, 
    16, 18, 15, 19, 19, 17, 17, 16, 15, 15, 15, 17, 12, 17, 17, 
    19, 14, 21, 19, 19, 18, 23, 18, 21, 18, 16, 17, 18, 13, 14, 
    17, 18, 16, 18, 16, 18, 18, 17, 17, 6, 22, 17, 18, 20, 18, 
    10, 18, 15, 10, 16, 16, 18, 18, 17, 21, 18, 18, 15, 13, 15, 
    17, 12, 16, 16, 16, 15, 20, 17, 14, 17, 17), format.spss = "F8.2", display_width = 10L), 
    mompa = structure(c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 
    0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 
    0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 
    1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 
    0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 
    0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 
    1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 
    0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 
    0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
    1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
    1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 
    0, 0, 1, 0, 0), format.spss = "F8.2", display_width = 10L), 
    momabhx = structure(c(0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 
    1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 
    1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 
    0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 
    0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 
    0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 
    1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 
    0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 
    1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 
    1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 
    1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 
    1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 0, 1, 0, 1), format.spss = "F8.2", display_width = 10L), 
    capiabr1 = structure(c(36, 43, NA, NA, 90, 95, 128, 137, 
    136, 245, 322, 154, 87, 111, 181, 278, 173, 137, 69, 24, 
    27, 70, 34, 27, 11, 53, 31, 49, 14, 54, 131, 35, 43, 43, 
    60, 58, 55, 60, 18, 38, 76, 98, 41, 20, 117, 58, 98, 10, 
    16, 101, 120, 165, 44, 96, 23, 19, 53, 57, 77, 41, 53, 100, 
    90, 96, 91, 29, 54, 134, 134, 105, 106, NA, 125, 61, 72, 
    34, 215, 42, NA, 106, 47, 45, 107, 208, 191, NA, 50, 56, 
    222, 47, 89, 134, 204, 211, 228, NA, 24, 34, 34, 135, 174, 
    112, 239, 104, 102, 129, 71, 100, 159, 280, 97, 105, NA, 
    56, 76, 120, 176, 89, 154, 46, 59, 214, 53, 245, 197, 60, 
    425, 25, 62, 137, 199, 171, 191, 46, 49, 117, 183, 79, 47, 
    76, NA, 158, 151, 47, 70, 118, 198, 94, 43, 296, 108, 56, 
    277, 214, 331, NA, 293, 277, 41, 134, 134, 283, 87, 96, 126, 
    305, 152, 82, 308, 168, 274, NA, 48, 171, 98, 90, 84, 257, 
    144, 255, NA, 106, 67, 184, 173, 156, 243, 357, 116, 132, 
    226, 260, 308, 358, 225, 312, 102, 244, 87, 176, 270, 224, 
    136, 243, NA, 117, 234, 280, 133, 143, 234, 273, NA, 169, 
    145, 310, 255, 280, 58, 152, 239, 254, 322, 342, 288, NA, 
    155, 179, 206, 270, 173, 319, 194, 206, 319, 111, 408, 310, 
    324, 296, 288, 391, 409, 379, 311, 338), format.spss = "F3.0", display_width = 11L), 
    cbclint = structure(c(51, 55, NA, NA, 65, 57, 46, 58, 53, 
    56, 75, 65, 33, NA, 65, NA, 51, 65, 34, 60, 45, 29, 43, 37, 
    65, 49, 56, 64, 53, 51, 39, 43, 64, 61, 74, 29, 60, 53, 45, 
    43, 45, 49, 47, 47, 66, 57, 73, 41, 56, 37, 65, 45, 53, 60, 
    53, 33, 43, 51, 53, 45, 47, 59, NA, 47, 79, 68, 56, 66, 70, 
    47, 63, 61, 61, 56, 33, 53, 56, 43, 51, 55, 51, 73, 56, 88, 
    56, 59, 30, 54, 82, 50, 63, 51, 58, 37, 67, 58, 51, 52, 40, 
    72, 63, NA, 43, 56, 60, 48, 66, NA, 55, 47, 61, 56, 55, 51, 
    55, 40, 64, 40, 66, 76, 45, 63, 53, 47, 51, 70, 80, 40, 53, 
    51, 43, 54, 64, 53, 64, 58, 56, 60, 55, 40, 40, 49, 48, 41, 
    47, 56, 60, 53, 55, 49, 55, 33, 67, 58, 41, 46, 67, 63, 64, 
    73, 73, 60, 49, 40, 51, 45, 53, 49, 65, 54, 58, 51, 68, 45, 
    41, 53, 60, 55, 61, 66, 69, 66, 67, 70, 66, NA, 56, 58, 61, 
    67, 73, 47, 74, 65, 62, 72, 59, 60, 73, 64, 48, 56, 53, 81, 
    65, 65, 65, 65, 59, 56, 70, 68, 63, 64, 74, 60, 75, 58, 63, 
    43, 72, 69, 59, 71, 71, 64, 66, 63, 46, 66, 66, 66, 53, NA, 
    73, 68, 65, 68, 62, 57, 68, 69, 74, 65, 78, 47), format.spss = "F8.0", display_width = 10L), 
    bpsidrr1 = structure(c(NA, 21, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, 18, NA, NA, NA, 7, 7, 7, 7, 7, 7, 7, 
    7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 8, 9, 10, 10, 10, 11, 
    11, 11, 9, 11, 8, 11, 9, 10, 12, 11, 13, 10, 8, 11, 10, 13, 
    12, 14, 9, 10, 13, 11, 11, 10, 13, 13, 13, 12, 10, 11, 13, 
    10, 13, 16, 12, 15, 10, 12, 13, 13, 11, 14, 15, 13, 13, 14, 
    13, 14, 13, 18, 13, 14, 14, 14, 15, 16, 17, 16, 14, 15, 14, 
    14, 15, 14, 20, 16, 16, 13, 17, 16, 15, 14, 16, 18, 17, 17, 
    19, 14, 17, 16, 16, 17, 16, 14, 14, 15, 17, 18, 17, 14, 14, 
    18, 17, 19, 16, 16, 17, 18, 15, 19, 16, 21, 18, 17, 19, 15, 
    20, 18, 19, 16, 18, 23, 15, 18, 20, 19, 12, 12, 21, 16, 17, 
    17, 20, 20, 19, 19, 22, 20, 19, 22, 14, 19, 19, 23, 19, 20, 
    19, 19, 20, 20, 23, 18, 19, 25, 20, 23, 20, 21, 22, 21, 21, 
    24, 22, 24, 22, 22, 18, 23, 24, 22, 22, 24, 21, 23, 21, 20, 
    21, 23, 23, 25, 24, 22, 23, 26, 23, 26, 26, 23, 26, 26, 23, 
    25, 24, 22, 27, 25, 24, 27, 23, 25, 25, 26, 23, 27, 30, 28, 
    29, 27, 31, 34, 32, 31, 34), format.spss = "F2.0", display_width = 11L), 
    ecbiir1 = structure(c(177, 197, 148, 133, 172, 133, 129, 
    NA, 159, 67, 141, 167, 111, 190, 174, NA, 137, 93, 99, 136, 
    54, 36, 36, 75, 126, 97, 68, 205, 110, NA, 109, 47, 93, 200, 
    183, 42, 73, 132, 82, 91, 154, 157, 82, 124, 207, 84, 188, 
    76, 104, 73, 185, 108, 140, 183, 52, 48, 100, 110, 109, 56, 
    88, 69, 189, 82, 210, 159, 68, 144, 119, 81, 190, 180, 199, 
    206, 72, 153, 151, NA, 115, 111, NA, 161, 118, 159, 127, 
    124, 136, 174, 232, 48, 161, 54, 74, 53, NA, 112, 148, 135, 
    137, 159, 75, 74, 36, 101, 142, 83, 132, 99, 141, 117, 117, 
    134, 105, 134, 147, 54, 206, 170, 69, 134, 64, 55, 129, 79, 
    110, 173, 159, 113, 163, 139, 111, 103, 93, 86, 179, 144, 
    167, 118, 124, 118, 91, 166, 66, 127, 54, 177, 108, 125, 
    115, 142, 130, 156, 152, 51, 132, 76, 155, 185, 148, 132, 
    146, 147, 134, 50, 158, 143, 142, 98, 111, 150, 138, NA, 
    221, 150, 167, 145, 146, 63, 201, 195, 192, 183, 168, 162, 
    170, NA, 87, 119, 171, 136, 66, 183, 162, NA, 168, 153, 151, 
    109, 147, 214, 156, 147, 148, 117, NA, 140, 124, 165, 175, 
    106, 198, 141, 183, 208, 201, 139, 171, 170, 165, 116, 226, 
    102, 157, 182, 161, 169, 208, 144, 140, 139, 128, 174, 158, 
    231, 168, 181, 211, 176, 159, 180, 110, 188, 151, 206, 205, 
    67), format.spss = "F3.0", display_width = 11L), mommhpsi = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 35.75, 32.75, 32.75, 32.75, 32.75, 38.5, 38.5, 
    32.75, 32.75, 32.75, 32.75, 34.25, 36.5, 43, 43, 49, 33, 
    38, NA, 33.5, 36.5, 36.75, 43.75, NA, 33.75, 50, 35.75, 49.25, 
    34, 39, 45.25, 50.75, 50, NA, NA, 34.25, 34.25, 34.25, 38.25, 
    42.75, NA, 34.5, 42.75, 36.25, 43, NA, 34.75, 34.75, 39.5, 
    39.5, 39, 48, NA, NA, 35, 35, 38.5, 50.5, NA, 41.5, 38.25, 
    43.5, 44.5, 43, 51.75, 44.5, NA, NA, NA, NA, 35.5, 38.5, 
    35.5, 38.5, 42.75, 50.25, NA, NA, NA, NA, NA, NA, 35.75, 
    35.75, 45, 40.5, 46, NA, NA, NA, NA, 47, 45.75, NA, NA, NA, 
    NA, NA, NA, NA, 47, 39.25, 50.75, 42.25, 42.25, 44.75, 44, 
    43.75, NA, NA, NA, NA, NA, NA, 45.75, 40.5, 38.25, 42.25, 
    51.75, NA, NA, NA, NA, NA, 39.75, 43.25, 50.5, 53.5, 54, 
    NA, 52.75, NA, 37.25, 41.5, 46.5, NA, 55.25, NA, 59.75, 42.25, 
    44.25, 44.25, 48.25, 47, NA, NA, NA, 46.5, 49.75, 50, 49.25, 
    56.25, NA, NA, NA, 39.75, 47, 44, 41, 54.75, 55.25, NA, NA, 
    38.25, 51, 48.75, NA, 43.75, 50.25, NA, NA, 46.25, 57, 59.75, 
    58.5, 62.5, 62.25, NA, NA, 46.75, 46, 56.25, 55, 55.75, 58.25, 
    NA, 44.75, 49.5, 46.5, 57.25, 53, 60.5, 63, NA, NA, NA, 56.75, 
    NA, 60.5, 43.75, 39.75, 59.25, 58.75, 57.5, 56.5, 63, NA, 
    NA, NA, NA, 55.5, 50, NA, 61.25, 61.5, 61, 62.75, 66.5, 57, 
    64.75, NA, 59.25, 68.25, 65.25, NA, 68.75, 50)), .Names = c("id", 
"peadiff", "ceadiff", "cdpea", "mompa", "momabhx", "capiabr1", 
"cbclint", "bpsidrr1", "ecbiir1", "mommhpsi"), row.names = c(NA, 
-246L), class = "data.frame")

Answer 1

您的代码工作正常。您正在使用的版本lavaan和版本给出的问题 。semTools按照Terrence D. Jorgensen（ 的作者之一）在此处semTools给出的建议，开始一个新的 R 会话并重新安装这两个包，如下所示：

install.packages("lavaan", repos = "http://www.da.ugent.be", type = "source")
# if necessary:    install.packages("devtools")
devtools::install_github("simsem/semTools/semTools")

现在命令：

fit5 <- runMI(model5, data = imputedData, fun="sem", ordered = "mompa")
summary(fit5, standardized = TRUE, ci = T)

给出以下输出：

Rubin's (1987) rules were used to pool point and SE estimates across 5 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.
lavaan.mi object based on 5 imputed data sets. 
See class?lavaan.mi help page for available methods. 

Convergence information:
The model converged on 5 imputed data sets 


Parameter Estimates:

  Information                                 Expected
  Information saturated (h1) model   
  Standard Errors                           Robust.sem

Regressions:
                   Estimate  Std.Err        t       df  P(>|z|) ci.lower ci.upper   Std.lv  Std.all
  ceadiff ~                                                                                        
    mompa             0.473    0.165    2.863 2016.256    0.004    0.149    0.797    0.473    0.223
    cdpea             0.137    0.038    3.589 2507.509    0.000    0.062    0.212    0.137    0.157
    momabhx          -0.251    0.302   -0.831      Inf    0.406   -0.843    0.341   -0.251   -0.059
  mompa ~                                                                                          
    peadiff   (b1)    0.108    0.035    3.091      Inf    0.002    0.039    0.176    0.108    0.245
    momabhx    (c)    0.548    0.165    3.324      Inf    0.001    0.225    0.871    0.548    0.273
    cdpea            -0.048    0.031   -1.525      Inf    0.127   -0.109    0.014   -0.048   -0.116
    mommhpsi  (b2)   -0.022    0.009   -2.365   61.332    0.021   -0.040   -0.003   -0.022   -0.192
 ...