我一直在尝试在 ZINB 模型中计算两个预测变量（主效应及其相互作用）的边际效应时应用集群稳健标准误差。当我不包含集群稳健的标准错误代码时，该代码可以完美运行，但是一旦包含它们，它就会停止返回置信区间和标准错误，并给出此错误：
无法计算预测的方差-协方差矩阵。不返回置信区间。
这是ZINB模型的代码：
moderation <- zeroinfl(y ~ scale(x)*scale(z), data=dat, dist = "negbin")
这是没有集群鲁棒标准误差估计器的边际效应的代码和生成的输出：
predict.1 <- ggpredict(moderation, c("x", "z"), ci.lvl = .99, type = "count"))
这是使用集群稳健标准误差估计器生成的代码和输出：
predict.2 <- ggpredict(moderation, c("x", "z"), ci.lvl = .99, type = "count", vcov.fun = "vcovCR", vcov.type = "CR1", vcov.args = list(cluster = dat$Family_ID))
我查看了 ggpredict 代码，以了解我的数据是否有任何内容不允许填充方差-协方差矩阵，并且它似乎没有违反任何必要条件（例如，没有分类预测变量）。此外，我将集群稳健的 SE 应用于该模型来创建模型的汇总表，并且它有效。
这是数据的输出：
> dput(dat)
structure(list(Subject = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 61,
62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95,
96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,
123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,
162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,
175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188,
189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199), x = c(23,
26, 33, 29, 26, 25, 25, 28, 32, 28, 29, 31, 21, 35, 24, 22, 29,
33, 31, 25, 34, 31, 22, 18, 32, 31, 22, 35, 28, 35, 30, 25, 25,
27, 31, 27, 31, 39, 24, 30, 34, 30, 33, 27, 18, 33, 26, 31, 28,
30, 24, 26, 19, 24, 39, 28, 33, 29, 29, 31, 12, 40, 33, 30, 30,
32, 30, 25, 28, 34, 23, 27, 27, 38, 30, 33, 31, 25, 26, 25, 31,
31, 32, 30, 29, 37, 24, 26, 37, 25, 20, 31, 26, 33, 33, 27, 27,
22, 20, 29, 24, 29, 27, 24, 30, 32, 24, 30, 29, 30, 28, 22, 28,
27, 26, 24, 26, 28, 29, 32, 25, 28, 28, 33, 30, 21, 27, 33, 29,
21, 26, 33, 33, 24, 33, 33, 31, 33, 28, 37, 29, 25, 28, 29, 41,
28, 30, 25, 31, 35, 23, 35, 27, 31, 23, 30, 28, 31, 22, 28, 25,
23, 29, 30, 21, 30, 41, 28, 36, 32, 37, 27, 23, 33, 24, 36, 31,
27, 33, 34, 35, 30, 30, 27, 27, 34, 36, 19, 33, 32, 25, 32, 27
), z = c(70.46, 83.89, 67.42, 104.57, 119.19,
52.93, 88.74, 53.75, 80.72, 70.14, 50.94, 87.73, 99.31, 87.69,
77.45, 90.41, 68.7, 94.02, 45.59, 70.28, 87.11, 94.11, 69.49,
41.99, 94.18, 57.94, 57.18, 95.37, 47.78, 93.5, 86.28, 97.97,
90.16, 89.01, 77.77, 53.15, 102.49, 104.58, 91.7, 91.64, 92.96,
93.36, 98.4, 109.39, 44.37, 50.79, 76.25, 81.32, 94.34, 76.66,
57.61, 83.24, 93.43, 59.24, 65.69, 94.37, 92.21, 92.72, 48.3,
78.65, 94.93, 92.33, 80.66, 65.85, 91.99, 85.82, 95.77, 83.65,
78.06, 93.11, 85.11, 61.33, 70.99, 97.38, 83.67, 81.34, 82.92,
103.1, 45.08, 77.64, 96.58, 57.1, 52.77, 40.59, 82.16, 40.62,
52.43, 44.77, 93.89, 95.18, 86.33, 55.67, 92.75, 71.19, 54.34,
96.2, 77.59, 69.71, 107.29, 83.38, 101.98, 64.54, 87.14, 63.51,
109.1, 89.68, 93.06, 74.91, 65.96, 93.49, 60.39, 35.44, 86.24,
92.8, 62.21, 91.13, 89.97, 57.97, 90.5, 84.36, 84.46, 75.68,
67.19, 65.98, 91.15, 51.78, 38.87, 77.26, 56.89, 76.78, 45.34,
80.92, 92.76, 101.21, 82.55, 53.12, 89.12, 52.03, 54.68, 73.25,
73.09, 93.29, 89.01, 57.14, 83.94, 93.31, 81.17, 89.39, 82.38,
65.19, 109.35, 89.68, 83.94, 102.96, 92.75, 81.65, 65.74, 62.3,
87.6, 101.99, 103.41, 101.25, 75.94, 104.26, 83.23, 49.33, 98.06,
81.44, 96.57, 94.94, 102.42, 102.83, 95.46, 91.62, 41.27, 74.82,
94.93, 84.2, 88.06, 77.49, 65.99, 46.02, 90.97, 78.3, 94.52,
93.04, 69.65, 53.39, 71.94, 75.18, 67.98, 48.61, 80.52), y = c(7,
8, 3, 0, 0, 4, 10, 0, 4, 0, 9, 2, 5, 0, 4, 5, 4, 3, 0, 4, 0,
6, 2, 5, 0, 2, 2, 0, 2, 2, 4, 5, 2, 4, 0, 0, 8, 3, 3, 4, 2, 2,
0, 2, 15, 0, 3, 6, 3, 4, 3, 11, 4, 3, 2, 5, 0, 0, 2, 2, 6, 0,
3, 0, 3, 0, 6, 0, 0, 0, 6, 7, 3, 0, 0, 3, 7, 5, 4, 3, 0, 0, 4,
0, 6, 7, 15, 14, 0, 3, 4, 0, 5, 0, 2, 2, 6, 8, 4, 3, 0, 5, 5,
3, 0, 0, 6, 0, 0, 6, 14, 17, 2, 5, 5, 5, 0, 2, 2, 3, 4, 2, 7,
6, 3, 5, 2, 0, 5, 9, 5, 5, 0, 14, 2, 4, 0, 4, 2, 3, 7, 2, 3,
0, 0, 9, 5, 7, 4, 0, 7, 4, 4, 0, 9, 4, 0, 2, 4, 2, 3, 2, 1, 2,
7, 8, 2, 4, 4, 5, 12, 5, 5, 2, 14, 1, 4, 5, 1, 8, 2, 1, 5, 5,
1, 0, 1, 10, 1, 8, 2, 1, 9), Family_ID = c("52259_82122", "56037_85858",
"51488_81352", "51730_81594", "52813_82634", "51283_52850_81149",
"51969_81833", "51330_81195", "52385_82248", "52198_82061", "51279_81145",
"51977_81841", "52018_81882", "52901_82723", "51679_81543", "56077_85897",
"52838_82659", "51469_81334", "51418_81283", "55895_85715", "51351_81216",
"56105_85925", "52198_82061", "51537_52707_81401", "51343_81208",
"51678_81542", "55810_85631", "54643_84465", "51283_52850_81149",
"51451_81316", "51820_81684", "51527_81391", "55695_85517", "52925_82747",
"52134_81998", "52321_82184", "51527_81391", "51468_81333", "56183_86002",
"51676_81540", "52941_82763", "55705_85527", "52493_82328_82671",
"54628_84450", "56022_85843_99973", "52921_82743", "56200_86019",
"51566_81430", "52925_82747", "51553_81417", "52586_99991_99992_99993",
"52054_81918", "51750_81614", "52270_82133", "54572_84394", "51303_81168",
"56094_85914", "55741_85562", "51381_81246", "51476_81340", "52076_81940",
"52662_82481", "53251_83073", "51340_81205", "51451_81316", "51798_81662",
"51424_81289", "55895_85715", "52332_82195", "51750_81614", "52110_81974",
"51752_81616", "51433_81298", "55703_85525", "51989_81853", "52872_82694",
"53303_83125", "99996_99997", "51354_81219_82525_82526", "52158_82021",
"52823_82644", "51305_81170", "53251_83073", "56016_85837_99974",
"55892_85712", "52586_99991_99992_99993", "52757_82578", "52769_82590_99986",
"55793_85614", "52345_82208", "51455_81320", "51529_81393_82672",
"56120_85940", "51676_81540", "51421_81286", "52813_82634", "52514_52849_82348_99995",
"51419_81284", "56187_86006", "51295_81161", "56034_85855", "52063_81927",
"55701_85523", "51486_81350", "53171_82993", "51476_81340", "51324_81189",
"51618_81482", "52468_82308", "55706_85528", "55961_85781", "52410_82265_99979",
"51945_81809", "52907_82729", "51673_81537", "51421_81286", "51673_81537",
"51802_81666", "51592_81456", "51782_81646", "51392_81257", "51318_81183",
"52496_82331", "51916_81780", "56073_85894", "51106_80975", "56022_85843_99973",
"51644_81508_99994", "51485_81349", "52761_82582_99976", "51529_81393_82672",
"51798_81662", "51351_81216", "51837_81701", "56094_85914", "51304_81169",
"51477_81341", "55646_85468", "52338_82201", "51294_81160", "55892_85712",
"52228_82091", "52526_82359", "51529_81393_82672", "51300_81166",
"51831_81695", "56150_85970", "51593_81457", "52875_82697", "51347_81212",
"51707_81571", "51833_81697", "51678_81542", "52194_82057", "52110_81974",
"51299_81165", "52069_81933", "55705_85527", "51478_81342", "55766_85587",
"51559_81423", "52809_82630", "52941_82763", "55694_85516", "51784_81648",
"56016_85837_99974", "51527_81391", "52251_82114", "55686_85508",
"51480_81344", "52364_82227", "55825_85646", "53000_82822", "51613_81477",
"52564_82389", "51639_81503", "52343_82206", "51833_81697", "52926_82748",
"51394_81259", "51716_82528", "51380_81245", "51555_81419", "51336_81201",
"51336_81201", "52844_82665", "52228_82091", "56083_85903", "51460_81325",
"52006_81870", "51956_81820", "51672_81536", "55788_85609")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -193L))
不幸的是，我无法使用
ggeffectsmarginaleffectspredictions()vcov?marginaleffects::predictionslibrary(pscl)
library(marginaleffects)
moderation <- zeroinfl(y ~ scale(x)*scale(z), data=dat, dist = "negbin")
predictions(moderation, vcov = ~Family_ID)
#
# Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
# 5.57 0.446 12.47 <0.001 116.1 4.69 6.44
# 4.16 0.256 16.28 <0.001 195.6 3.66 4.66
# 2.50 0.357 7.01 <0.001 38.6 1.80 3.20
# 3.19 0.431 7.40 <0.001 42.7 2.34 4.03
# 3.32 0.525 6.33 <0.001 31.9 2.29 4.35
# --- 183 rows omitted. See ?avg_predictions and ?print.marginaleffects ---
# 2.50 0.308 8.13 <0.001 51.0 1.90 3.10
# 2.75 0.269 10.22 <0.001 79.0 2.22 3.27
# 4.95 0.359 13.79 <0.001 141.3 4.25 5.65
# 2.81 0.621 4.52 <0.001 17.3 1.59 4.02
# 3.99 0.235 17.00 <0.001 212.9 3.53 4.45
# Columns: rowid, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, y, x, z
请注意，默认情况下，
predictions()newdatadatagrid()?datagridpredictions(moderation,
vcov = ~Family_ID,
newdata = datagrid(x = c(12, 42), z = c(67, 78)))
#
# Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 % x z
# 11.551 2.576 4.48 < 0.001 17.1 6.5019 16.60 12 67
# 9.110 1.623 5.61 < 0.001 25.6 5.9283 12.29 12 78
# 0.765 0.365 2.10 0.03593 4.8 0.0503 1.48 42 67
# 0.802 0.311 2.58 0.00998 6.6 0.1919 1.41 42 78
#
# Columns: rowid, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, y, x, z
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