Comments (4)
strengejacke
commented on August 17, 2024
Do you have a reproducible example?
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jpcsuarez
commented on August 17, 2024
Thanks for the quick response. Kindly see the following example. x1 (or the first variable of the zero-component) gets omitted. The same thing happens to my larger hurdle/zero-inflated glmmTMB files.
library(glmmTMB)
library(performance)
hnb <- readRDS(file='dummy_glmmTMB.RDS')
summary(hnb)
Family: truncated_nbinom2 ( log )
Formula: retweet_count ~ 1 + x1 + x2 + x3
Zero inflation: retweet_count ~ 1 + x1 + x2 + x3
Data: df
AIC BIC logLik deviance df.resid
297794.3 297888.2 -148888.2 297776.3 249991
Dispersion parameter for truncated_nbinom2 family (): 5.62e-10
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -15.707384 229.293433 -0.069 0.9454
x1 -0.436397 0.048344 -9.027 <2e-16 ***
x2 -0.015658 0.007164 -2.186 0.0289 *
x3 -0.147176 0.007903 -18.624 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.200223 0.009541 230.62 <2e-16 ***
x1 -0.242178 0.019934 -12.15 <2e-16 ***
x2 -0.044450 0.002258 -19.69 <2e-16 ***
x3 0.227055 0.007253 31.30 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
check_collinearity(hnb)
# Check for Multicollinearity
* conditional component:
Low Correlation
Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
x1 1.07 [1.07, 1.08] 1.03 0.93 [0.93, 0.94]
x2 1.19 [1.18, 1.19] 1.09 0.84 [0.84, 0.85]
x3 1.11 [1.11, 1.12] 1.05 0.90 [0.90, 0.90]
* zero inflated component:
Low Correlation
Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
x2 1.04 [1.03, 1.04] 1.02 0.96 [0.96, 0.97]
x3 1.03 [1.02, 1.03] 1.01 0.97 [0.97, 0.98]
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strengejacke
commented on August 17, 2024
Thanks! You misspecified the ziformula - glmmTMB should throw a warning indicating that the ziformula doesn't require an outcome. Removing the LHS from the ziformula fixes your issue:
library(glmmTMB)
library(performance)
hnb <- readRDS(file = "c:/Users/mail/Desktop/dummy_glmmTMB.RDS")
d <- insight::get_data(hnb, verbose = FALSE)
m <- glmmTMB(
retweet_count ~ x1 + x2 + x3,
data = d,
family = truncated_nbinom2,
ziformula = ~ x1 + x2 + x3,
dispformula = ~1
)
check_collinearity(m)
#> # Check for Multicollinearity
#>
#> * conditional component:
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> x1 1.07 [1.07, 1.08] 1.03 0.93 [0.93, 0.94]
#> x2 1.19 [1.18, 1.19] 1.09 0.84 [0.84, 0.85]
#> x3 1.11 [1.11, 1.12] 1.05 0.90 [0.90, 0.90]
#>
#> * zero inflated component:
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> x1 1.04 [1.03, 1.04] 1.02 0.96 [0.96, 0.97]
#> x2 1.03 [1.02, 1.03] 1.01 0.97 [0.97, 0.98]
#> x3 1.02 [1.01, 1.02] 1.01 0.98 [0.98, 0.99]
m <- glmmTMB(
retweet_count ~ x1 + x2 + x3,
data = d,
family = truncated_nbinom2,
ziformula = retweet_count ~ x1 + x2 + x3,
dispformula = ~1
)
#> Warning in f(init, x[[i]]): discarding LHS of second argument
check_collinearity(m)
#> # Check for Multicollinearity
#>
#> * conditional component:
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> x1 1.07 [1.07, 1.08] 1.03 0.93 [0.93, 0.94]
#> x2 1.19 [1.18, 1.19] 1.09 0.84 [0.84, 0.85]
#> x3 1.11 [1.11, 1.12] 1.05 0.90 [0.90, 0.90]
#>
#> * zero inflated component:
#>
#> Low Correlation
#>
#> Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
#> x2 1.04 [1.03, 1.04] 1.02 0.96 [0.96, 0.97]
#> x3 1.03 [1.02, 1.03] 1.01 0.97 [0.97, 0.98]Created on 2023-12-21 with reprex v2.0.2
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jpcsuarez
commented on August 17, 2024
Ah my mistake then, thanks so much! Everything works now.
Maybe one last question: will check_overdispersion() be available someday for pscl based models?
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