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.
m1 <- lmer(distance ~ age + Sex + (1 | Subject), data = Orthodont)
m2 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
tab_model(m1, m2)
|
|
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.
tab_model(m1, p.val = "kr", show.df = TRUE)
|
|
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
|
|
τ00 Subject
|
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.
data("efc")
efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1)
efc$cluster <- as.factor(efc$e15relat)
m3 <- glmer(
neg_c_7d ~ c160age + c161sex + e42dep + (1 | cluster),
data = efc,
family = binomial(link = "logit")
)
tab_model(m1, m3)
|
|
distance
|
neg c 7 d
|
|
Predictors
|
Estimates
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
17.71
|
16.07 – 19.34
|
<0.001
|
0.02
|
0.01 – 0.05
|
<0.001
|
|
age
|
0.66
|
0.54 – 0.78
|
<0.001
|
|
|
|
|
SexFemale
|
-2.32
|
-3.81 – -0.83
|
0.002
|
|
|
|
|
carer’age
|
|
|
|
1.01
|
0.99 – 1.02
|
0.355
|
|
carer’s gender
|
|
|
|
1.83
|
1.30 – 2.59
|
0.001
|
|
elder’s dependency
|
|
|
|
2.37
|
1.99 – 2.81
|
<0.001
|
|
Random Effects
|
|
σ2
|
2.05
|
3.29
|
|
τ00
|
3.27 Subject
|
0.24 cluster
|
|
ICC
|
0.61 Subject
|
0.07 cluster
|
|
Observations
|
108
|
888
|
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
0.181 / 0.237
|
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.
library(glmmTMB)
data("Salamanders")
m4 <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined,
family = truncated_poisson(link = "log"),
data = Salamanders
)
tab_model(m1, m3, m4, show.ci = FALSE)
|
|
distance
|
neg c 7 d
|
count
|
|
Predictors
|
Estimates
|
p
|
Odds Ratios
|
p
|
Incidence Rate Ratios
|
p
|
|
(Intercept)
|
17.71
|
<0.001
|
0.02
|
<0.001
|
0.94
|
0.745
|
|
age
|
0.66
|
<0.001
|
|
|
|
|
|
SexFemale
|
-2.32
|
0.002
|
|
|
|
|
|
carer’age
|
|
|
1.01
|
0.355
|
|
|
|
carer’s gender
|
|
|
1.83
|
0.001
|
|
|
|
elder’s dependency
|
|
|
2.37
|
<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
|
0.24 cluster
|
0.05 site
|
|
ICC
|
0.61 Subject
|
0.07 cluster
|
0.05 site
|
|
Observations
|
108
|
888
|
644
|
|
Marginal R2 / Conditional R2
|
0.398 / 0.768
|
0.181 / 0.237
|
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