I am running a path analysis in lavaan (with ordinal) and would like to use imputed data.
But whether I impute data separately and use runMI or let the original data be imputed as a part of sem.mi command, I get same error:
Error: evaluation nested too deeply: infinite recursion / options(expressions=)?
Error during wrapup: evaluation nested too deeply: infinite recursion / options(expressions=)?
If I run: options(expressions = 100000) the error message changes to: Error: protect(): protection stack overflow
I tried to change
--max-ppsize=500000
but in the command line I can't access rstudio.exe (says: the system cannot find the path specified, - even though I double-checked the path:
C:\Program Files\RStudio\bin\rstudio.exe --max-ppsize=500000)
What can I do to run my analysis with imputed data or to impute it as a part of the path analyses estimation?
Here is my code:
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)
P.S. It prints summary with a warning in this scenario but doesn't print p-values or CIs, so I cannot determine what coefficients are 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
Thank you!
P.S. I don't know how to supply my data sample.
Here is the unimputed data output:
> 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")
Your code works correctly. The problem in given by the version of lavaan and semTools that you are using.
Following the suggestions given here by Terrence D. Jorgensen (one of the authors of semTools), start a new session of R and reinstall the two packages as follows:
install.packages("lavaan", repos = "http://www.da.ugent.be", type = "source")
# if necessary: install.packages("devtools")
devtools::install_github("simsem/semTools/semTools")
Now the commands:
fit5 <- runMI(model5, data = imputedData, fun="sem", ordered = "mompa")
summary(fit5, standardized = TRUE, ci = T)
give the following output:
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
...