Using cluster-robust SEs within a zero-inflated negative binomial model through ggpredict won't return confidence intervals

[nphillips36]

Hey all,

I've been stuck the last few days trying to apply cluster-robust standard errors in my calculation of the marginal effects of two predictor variables (main effects and their interaction) in a ZINB model. The code works perfectly when I don't include the cluster-robust standard error code, but as soon as I include them it stops returning confidence intervals and standard errors and gives this error "Could not compute variance-covariance matrix of predictions. No confidence intervals are returned."

Here is the code for the ZINB model:

moderation <- zeroinfl(ASR_Aggr_Raw ~ scale(NEO_A)*scale(CogCrystalComp_AgeAdj), data=dat, dist = "negbin")

Here is the code and generated output (screenshot) for marginal effects without the cluster-robust standard error estimator:

predict.1 <- ggpredict(moderation, c("NEO_A", "CogCrystalComp_AgeAdj"), ci.lvl = .99, type = "count"))

Here is the code and generated output (screenshot) with the estimator (which was taken almost exactly from the code's website https://strengejacke.github.io/ggeffects/articles/practical_robustestimation.html):

predict.2 <- ggpredict(moderation, c("NEO_A", "CogCrystalComp_AgeAdj"), ci.lvl = .99, type = "count", vcov.fun = "vcovCR", vcov.type = "CR1", vcov.args = list(cluster = dat$Family_ID))

Help in generating the CIs for the cluster-robust SEs would be incredibly appreciated, as I'm not sure where to go from here. Thank you in advance for your help!

[strengejacke]

do you have a reproducible example, possibly using some toy data?

[nphillips36]

Sure! Here is the model and its application using ggpredict:

moderation <- zeroinfl(y ~ scale(x)*scale(z), data=dat, dist = "negbin")

ggpredict(moderation, c("x", "z"), ci.lvl = .99, type = "count", vcov.fun = "vcovCR", vcov.type = "CR1", vcov.args = list(cluster = dat$Family_ID))

And here is the data

> 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", 
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"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))

[strengejacke]

Thanks! Looks like a duplicate of #360?

[nphillips36]

Yes, exactly. Vincent was basing that question off this original post (which I also posted on stackoverflow https://stackoverflow.com/questions/76780457/using-cluster-robust-ses-within-a-zero-inflated-negative-binomial-model-through).

[strengejacke]

Ok, closing in favor of #360