This vignette shows examples for using tab_model()
to create HTML tables for mixed models. Basically, tab_model()
behaves in a very similar way for mixed models as for other, simple regression models, as shown in this vignette.
Mixed models summaries as HTML table
Unlike tables for non-mixed models, tab_models()
adds additional information on the random effects to the table output for mixed models. You can hide these information with show.icc = FALSE
and show.re.var = FALSE
. Furthermore, the R-squared values are marginal and conditional R-squared statistics, based on Nakagawa et al. 2017.
|
distance
|
Reaction
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
(Intercept)
|
17.71
|
16.07 – 19.34
|
<0.001
|
251.41
|
238.03 – 264.78
|
<0.001
|
age
|
0.66
|
0.54 – 0.78
|
<0.001
|
|
|
|
SexFemale
|
-2.32
|
-3.81 – -0.83
|
0.002
|
|
|
|
Days
|
|
|
|
10.47
|
7.44 – 13.50
|
<0.001
|
Random Effects
|
σ2
|
2.05
|
654.94
|
τ00
|
3.27 Subject
|
612.09 Subject
|
τ11
|
|
35.07 Subject.Days
|
ρ01
|
|
0.07 Subject
|
ICC
|
0.61 Subject
|
0.48 Subject
|
Observations
|
108
|
180
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
0.279 / 0.799
|
The marginal R-squared considers only the variance of the fixed effects, while the conditional R-squared takes both the fixed and random effects into account.
The p-value is a simple approximation, based on the t-statistics and using the normal distribution function. A more precise p-value can be computed using p.val = "kr"
. In this case, which only applies to linear mixed models, the computation of p-values is based on conditional F-tests with Kenward-Roger approximation for the degrees of freedom (using the using the pbkrtest-package). Note that here the computation is more time consuming and thus not used as default. You can also display the approximated degrees of freedom with show.df
.
|
distance
|
Predictors
|
Estimates
|
CI
|
p
|
df
|
(Intercept)
|
17.71
|
16.34 – 19.08
|
<0.001
|
99.00
|
age
|
0.66
|
0.56 – 0.76
|
<0.001
|
80.00
|
SexFemale
|
-2.32
|
-3.57 – -1.07
|
0.005
|
25.00
|
Random Effects
|
σ2
|
2.05
|
τ00Subject
|
3.27
|
ICC Subject
|
0.61
|
Observations
|
108
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
Generalized linear mixed models
tab_model()
can also print and combine models with different link-functions.
|
distance
|
cbind(incidence,size-incidence)
|
Predictors
|
Estimates
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
(Intercept)
|
17.71
|
16.07 – 19.34
|
<0.001
|
0.25
|
0.16 – 0.39
|
<0.001
|
age
|
0.66
|
0.54 – 0.78
|
<0.001
|
|
|
|
SexFemale
|
-2.32
|
-3.81 – -0.83
|
0.002
|
|
|
|
period 2
|
|
|
|
0.37
|
0.20 – 0.67
|
0.001
|
period 3
|
|
|
|
0.32
|
0.17 – 0.61
|
<0.001
|
period 4
|
|
|
|
0.21
|
0.09 – 0.47
|
<0.001
|
Random Effects
|
σ2
|
2.05
|
3.29
|
τ00
|
3.27 Subject
|
0.41 herd
|
ICC
|
0.61 Subject
|
0.11 herd
|
Observations
|
108
|
56
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
0.085 / 0.187
|
More complex models
Finally, an example from the glmmTMB-package to show how easy it is to print zero-inflated generalized linear mixed models as HTML table.
|
distance
|
cbind(incidence,size-incidence)
|
count
|
Predictors
|
Estimates
|
p
|
Odds Ratios
|
p
|
Incidence Rate Ratios
|
p
|
(Intercept)
|
17.71
|
<0.001
|
0.25
|
<0.001
|
0.94
|
0.745
|
age
|
0.66
|
<0.001
|
|
|
|
|
SexFemale
|
-2.32
|
0.002
|
|
|
|
|
period 2
|
|
|
0.37
|
0.001
|
|
|
period 3
|
|
|
0.32
|
<0.001
|
|
|
period 4
|
|
|
0.21
|
<0.001
|
|
|
sppPR
|
|
|
|
|
0.59
|
0.062
|
sppDM
|
|
|
|
|
1.25
|
0.121
|
sppEC-A
|
|
|
|
|
0.82
|
0.331
|
sppEC-L
|
|
|
|
|
1.91
|
<0.001
|
sppDES-L
|
|
|
|
|
1.83
|
<0.001
|
sppDF
|
|
|
|
|
1.05
|
0.765
|
minedno
|
|
|
|
|
2.76
|
<0.001
|
Zero-Inflated Model
|
(Intercept)
|
|
|
|
|
5.79
|
<0.001
|
sppPR
|
|
|
|
|
5.36
|
<0.001
|
sppDM
|
|
|
|
|
0.65
|
0.223
|
sppEC-A
|
|
|
|
|
3.02
|
0.003
|
sppEC-L
|
|
|
|
|
0.65
|
0.223
|
sppDES-L
|
|
|
|
|
0.51
|
0.056
|
sppDF
|
|
|
|
|
0.65
|
0.223
|
minedno
|
|
|
|
|
0.09
|
<0.001
|
Random Effects
|
σ2
|
2.05
|
3.29
|
1.00
|
τ00
|
3.27 Subject
|
0.41 herd
|
0.05 site
|
ICC
|
0.61 Subject
|
0.11 herd
|
0.05 site
|
Observations
|
108
|
56
|
644
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
0.085 / 0.187
|
0.510 / 0.577
|
References
Nakagawa S, Johnson P, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisted and expanded. J. R. Soc. Interface 14. doi: 10.1098/rsif.2017.0213