The goal
I want to run dominance analysis on a mixed-effects beta model, to approximate the relative importance of a set of predictors (2 factors, 1 scaled continuous, 1 continuous with splines). The random part of the beta model would be random intercepts, with a nested structure: (1|random1/random2).
The mixed-effects beta would be fitted with glmmTMB::glmmTMB(), the metric of model quality would be marginal R2 (MuMIn:.r.squaredGLMM()[1]), and dominance analysis would be with the domir::domin() package.
The obstacle
I get errors when trying to run domir::domin() on the mixed-effects beta model, with two different errors messages, one without nested effects: Error: Invalid grouping factor specification; another when there is nesting: “Error: unparseable formula for grouping factor”. A model without random effects works fine. I get warnings from MuMIn about small mu and that the effects of zero-inflation and dispersion were ignored, but I don't think these are the real obstacle.
A reproducible example
Building on a subset of the real data throwing the errors, I try to provide a reproducible example. A question on how to run dominance analyses with a mixed-effects model has been asked before, but I haven’t found a solution there.
I hope this catches the attention of Josep Luchman, author of the domir package, who I have seen to be very active in this forum :) Many thanks in advance, and thank you for the amazing package!
Cheers!
#### Load libraries
library(domir)
#> Warning: package 'domir' was built under R version 4.2.3
library(glmmTMB)
library(MuMIn)
#> Warning: package 'MuMIn' was built under R version 4.2.3
library(splines)
This dataset is an "anonymised" subset of my data, which looks ok to reproduce the errors
df <- structure(list(response = c(0.21, 0.151, 0.256, 0.011, 0.26,
0.176, 0.087, 0.064, 0.123, 0.312, 0.148, 0.001, 0.099, 0.265,
0.222, 0.07, 0.171, 0.108, 0.115, 0.119, 0.2, 0.01, 0.092, 0.042,
0.067, 0.389, 0.035, 0.046, 0.041, 0.143, 0.042, 0.154, 0.351,
0.047, 0.068, 0.1, 0.062, 0.009, 0.224, 0.114, 0.063, 0.399,
0.026, 0.047, 0.07, 0.205, 0.31, 0.204, 0.092, 0.133, 0.209,
0.012, 0.022, 0.207, 0.233, 0.103, 0.072, 0.344, 0.063, 0.216,
0.302, 0.43, 0.046, 0.205, 0.082, 0.007, 0.027, 0.022, 0.036,
0.095, 0.192, 0.146, 0.184, 0.072, 0.122, 0.029, 0.043, 0.032,
0.312, 0.058), cont1 = c(121, 261, 140, 162, 47, 118, 47, 74,
184, 162, 141, 184, 37, 184, 123, 146, 43, 220, 176, 107, 171,
82, 171, 49, 162, 151, 40, 170, 111, 47, 61, 74, 177, 130, 147,
125, 28, 35, 158, 51, 184, 88, 110, 184, 68, 78, 62, 46, 138,
162, 116, 51, 127, 199, 51, 52, 114, 91, 184, 244, 243, 115,
72, 117, 151, 39, 56, 30, 70, 41, 79, 171, 126, 114, 34, 184,
62, 82, 60, 34), cont2 = c(0.52, 0.42, 0.301, 0.515, 0.271, 0.517,
0.319, 0.375, 0.446, 0.406, 0.53, 0.581, 0.245, 0.313, 0.295,
0.21, 0.35, 0.501, 0.434, 0.495, 0.312, 0.437, 0.473, 0.563,
0.442, 0.268, 0.546, 0.543, 0.579, 0.482, 0.378, 0.521, 0.259,
0.586, 0.555, 0.474, 0.404, 0.587, 0.392, 0.389, 0.423, 0.31,
0.533, 0.475, 0.41, 0.329, 0.389, 0.45, 0.502, 0.41, 0.428, 0.563,
0.56, 0.542, 0.498, 0.554, 0.558, 0.331, 0.423, 0.406, 0.319,
0.257, 0.562, 0.453, 0.598, 0.436, 0.468, 0.49, 0.254, 0.493,
0.495, 0.346, 0.364, 0.558, 0.29, 0.531, 0.382, 0.402, 0.389,
0.42), factor1 = structure(c(2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L), levels = c("1", "2"), class = "factor"),
factor2 = structure(c(2L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 1L, 2L, 3L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
3L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 2L, 3L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L), levels = c("1",
"2", "3"), class = "factor"), rand1 = structure(c(33L, 19L,
2L, 24L, 11L, 24L, 16L, 15L, 13L, 7L, 35L, 24L, 7L, 5L, 4L,
31L, 29L, 7L, 7L, 8L, 18L, 12L, 13L, 8L, 27L, 8L, 1L, 24L,
9L, 6L, 22L, 24L, 17L, 17L, 7L, 10L, 34L, 21L, 8L, 23L, 13L,
8L, 9L, 30L, 25L, 29L, 13L, 8L, 26L, 24L, 24L, 35L, 7L, 24L,
8L, 8L, 33L, 8L, 13L, 24L, 32L, 14L, 20L, 7L, 8L, 2L, 24L,
29L, 24L, 24L, 8L, 8L, 24L, 33L, 8L, 28L, 3L, 8L, 8L, 24L
), levels = c("01", "02", "03", "04", "05", "06", "07", "08",
"09", "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"), class = "factor"),
rand2 = structure(c(7L, 38L, 28L, 59L, 51L, 61L, 10L, 53L,
52L, 62L, 37L, 58L, 42L, 12L, 47L, 41L, 35L, 24L, 15L, 9L,
55L, 8L, 1L, 21L, 6L, 57L, 20L, 3L, 5L, 18L, 19L, 50L, 48L,
39L, 15L, 43L, 23L, 11L, 29L, 31L, 52L, 60L, 5L, 34L, 45L,
35L, 25L, 40L, 56L, 16L, 59L, 37L, 15L, 59L, 21L, 54L, 7L,
40L, 52L, 33L, 46L, 4L, 26L, 15L, 2L, 30L, 44L, 14L, 3L,
61L, 60L, 27L, 36L, 7L, 32L, 13L, 49L, 17L, 60L, 22L), levels = c("01",
"02", "03", "04", "05", "06", "07", "08", "09", "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", "55", "56", "57", "58", "59", "60", "61",
"62"), class = "factor")), class = "data.frame", row.names = c(NA,
-80L))
str(df)
#> 'data.frame': 80 obs. of 7 variables:
#> $ response: num 0.21 0.151 0.256 0.011 0.26 0.176 0.087 0.064 0.123 0.312 ...
#> $ cont1 : num 121 261 140 162 47 118 47 74 184 162 ...
#> $ cont2 : num 0.52 0.42 0.301 0.515 0.271 0.517 0.319 0.375 0.446 0.406 ...
#> $ factor1 : Factor w/ 2 levels "1","2": 2 2 2 1 2 1 2 2 2 1 ...
#> $ factor2 : Factor w/ 3 levels "1","2","3": 2 3 3 2 3 1 3 2 1 2 ...
#> $ rand1 : Factor w/ 35 levels "01","02","03",..: 33 19 2 24 11 24 16 15 13 7 ...
#> $ rand2 : Factor w/ 62 levels "01","02","03",..: 7 38 28 59 51 61 10 53 52 62 ...
Test model-fitting functions and metric of model quality
mod <- glmmTMB::glmmTMB(response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2 + (1|rand1/rand2), dispformula = ~ 1, data = df, family = beta_family(link = "logit"))
summary(mod)
#> Family: beta ( logit )
#> Formula:
#> response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2 +
#> (1 | rand1/rand2)
#> Data: df
#>
#> AIC BIC logLik deviance df.resid
#> -184.6 -158.4 103.3 -206.6 69
#>
#> Random effects:
#>
#> Conditional model:
#> Groups Name Variance Std.Dev.
#> rand2:rand1 (Intercept) 0.13042 0.3611
#> rand1 (Intercept) 0.02887 0.1699
#> Number of obs: 80, groups: rand2:rand1, 62; rand1, 35
#>
#> Dispersion parameter for beta family (): 22.1
#>
#> Conditional model:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.73257 0.36252 -7.538 4.78e-14 ***
#> scale(cont2) -0.50095 0.08824 -5.677 1.37e-08 ***
#> bs(cont1, df = 3)1 2.02663 0.90326 2.244 0.0249 *
#> bs(cont1, df = 3)2 -0.25883 0.67948 -0.381 0.7033
#> bs(cont1, df = 3)3 1.53706 0.60982 2.521 0.0117 *
#> factor12 -0.14068 0.22337 -0.630 0.5288
#> factor22 0.06847 0.24910 0.275 0.7834
#> factor23 -0.08611 0.33131 -0.260 0.7949
#> ---
#> Signif. codes: 0 '*' 0.001 '' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MuMIn::r.squaredGLMM(mod)
#> Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.
#> Warning in r.squaredGLMM.glmmTMB(mod): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> R2m R2c
#> [1,] 0.4398609 0.6686709
domir::domin
Without any random effects
domir::domin(response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2,
reg = function(y) glmmTMB::glmmTMB(formula = y, dispformula = ~ 1, data = df, family = beta_family(link = "logit")),
fitstat = list((x) list(R2m = MuMIn::r.squaredGLMM(x)[[1]]), "R2m"))
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.2 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Overall Fit Statistic: 0.157678
#>
#> General Dominance Statistics:
#> General Dominance Standardized Ranks
#> scale(cont2) 0.1187231767 0.752947071 1
#> bs(cont1, df = 3) 0.0367193338 0.232875464 2
#> factor1 0.0014161674 0.008981390 3
#> factor2 0.0008193067 0.005196075 4
#>
#> Conditional Dominance Statistics:
#> IVs: 1 IVs: 2 IVs: 3 IVs: 4
#> scale(cont2) 1.165115e-01 0.120016192 0.1196132751 0.118751758
#> bs(cont1, df = 3) 3.379900e-02 0.037772258 0.0378437056 0.037462371
#> factor1 6.118088e-06 0.002503043 0.0018023728 0.001353136
#> factor2 1.239085e-03 0.002567201 0.0006211075 -0.001150167
#>
#> Complete Dominance Designations:
#> Dmnated?scale(cont2) Dmnated?bs(cont1, df = 3)
#> Dmnates?scale(cont2) NA TRUE
#> Dmnates?bs(cont1, df = 3) FALSE NA
#> Dmnates?factor1 FALSE FALSE
#> Dmnates?factor2 FALSE FALSE
#> Dmnated?factor1 Dmnated?factor2
#> Dmnates?scale(cont2) TRUE TRUE
#> Dmnates?bs(cont1, df = 3) TRUE TRUE
#> Dmnates?factor1 NA NA
#> Dmnates?factor2 NA NA
With random effects, without nesting
domir::domin(response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2 + (1|rand1),
reg = function(y) glmmTMB::glmmTMB(formula = y, dispformula = ~ 1, data = df, family = beta_family(link = "logit")),
fitstat = list((x) list(R2m = MuMIn::r.squaredGLMM(x)[[1]]), "R2m"),
consmodel = "(1|rand1)")
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in Ops.factor(1, rand1): '|' not meaningful for factors
#> Error: Invalid grouping factor specification, 1 | rand1
With random effects, with nesting
domir::domin(response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2 + (1|rand1/rand2),
reg = function(y) glmmTMB::glmmTMB(formula = y, dispformula = ~ 1, data = df, family = beta_family(link = "logit")),
fitstat = list((x) list(R2m = MuMIn::r.squaredGLMM(x)[[1]]), "R2m"),
consmodel = "(1|rand1/rand2)")
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Warning in r.squaredGLMM.glmmTMB(x): the effects of zero-inflation and
#> dispersion model are ignored
#> Warning: mu of 0.1 is too close to zero, estimate of random effect variances may
#> be unreliable.
#> Error: unparseable formula for grouping factor
Session info
sessionInfo()
#> R version 4.2.0 (2022-04-22 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22000)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=Spanish_Spain.utf8 LC_CTYPE=Spanish_Spain.utf8
#> [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C
#> [5] LC_TIME=Spanish_Spain.utf8
#>
#> attached base packages:
#> [1] splines stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] MuMIn_1.47.5 glmmTMB_1.1.3 domir_1.0.1
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.10 compiler_4.2.0 nloptr_2.0.1
#> [4] highr_0.9 TMB_1.8.1 tools_4.2.0
#> [7] boot_1.3-28 digest_0.6.29 lme4_1.1-29
#> [10] evaluate_0.15 lifecycle_1.0.3 nlme_3.1-157
#> [13] lattice_0.20-45 rlang_1.1.1 reprex_2.0.2
#> [16] Matrix_1.4-1 cli_3.6.1 rstudioapi_0.13
#> [19] yaml_2.3.5 mvtnorm_1.1-3 xfun_0.31
#> [22] fastmap_1.1.0 coda_0.19-4 withr_2.5.0
#> [25] stringr_1.5.0 knitr_1.39 fs_1.5.2
#> [28] vctrs_0.6.3 stats4_4.2.0 grid_4.2.0
#> [31] glue_1.6.2 survival_3.3-1 rmarkdown_2.14
#> [34] multcomp_1.4-19 TH.data_1.1-1 minqa_1.2.4
#> [37] magrittr_2.0.3 codetools_0.2-18 htmltools_0.5.2
#> [40] emmeans_1.8.5-9000004 MASS_7.3-57 insight_0.19.1.3
#> [43] xtable_1.8-4 numDeriv_2016.8-1.1 sandwich_3.0-1
#> [46] stringi_1.7.6 estimability_1.4.1 zoo_1.8-10
Created on 2023-07-25 with reprex v2.0.2
EDIT (2 August 2023)
Many thanks to Joseph Luchman!
Dominance analysis with a beta regression with random effects implemented with domir::domin() yields the same results as domir::domir(). See below the results using domir::domir() (reprex won't render it for some reason).
domir::domir(response ~ scale(cont2) + bs(cont1, df = 3) + factor1 + factor2,
function(y) {
glmmTMB::glmmTMB(formula = update(y, . ~ . +(1|rand1/rand2)), dispformula = ~ 1, data = df, family = beta_family(link = "logit")) %>% MuMIn::r.squaredGLMM() %>% .[[1]]
})
.alland.adjparse usingstats::terms()which will strip the parentheses from that term which causes it to fail as it's read byglmmTMBas1|family/genuswithout parentheses. The fix is to add it using anupdate()to the formula in the function itself. If you can't get it to work recommend asking as a separate question and I can expand on this answer. – jluchman Aug 01 '23 at 16:40