Introduction: Adjusted Predictions and Marginal Effects for Random Effects Models
Daniel Lüdecke
2023-01-24
Source:vignettes/introduction_randomeffects.Rmd
introduction_randomeffects.Rmd## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.5.3
## Current Matrix version is 1.5.1
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packageThis vignette shows how to calculate marginal effects that take the random-effect variances for mixed models into account.
Marginal effects for mixed effects models
Basically, the type of predictions, i.e. whether to account for the
uncertainty of random effects or not, can be set with the
type-argument. The default, type = "fixed",
means that predictions are on the population-level and do not account
for the random effect variances. Intervals are confidence
intervals for the predicted values.
library(ggeffects)
library(lme4)
data(sleepstudy)
m <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
pr <- ggpredict(m, "Days")
pr
#> # Predicted values of Reaction
#>
#> Days | Predicted | 95% CI
#> -----------------------------------
#> 0 | 251.41 | [238.03, 264.78]
#> 1 | 261.87 | [248.57, 275.17]
#> 2 | 272.34 | [258.44, 286.24]
#> 3 | 282.81 | [267.70, 297.91]
#> 5 | 303.74 | [284.96, 322.52]
#> 6 | 314.21 | [293.17, 335.24]
#> 7 | 324.68 | [301.21, 348.14]
#> 9 | 345.61 | [316.94, 374.28]
#>
#> Adjusted for:
#> * Subject = 0 (population-level)
plot(pr)
When type = "random", the predicted values are still
on the population-level. However, the random effect variances are
taken into account, meaning that the intervals are actually
prediction intervals and become larger. More technically
speaking, type = "random" accounts for the uncertainty of
the fixed effects conditional on the estimates of the
random-effect variances and conditional modes (BLUPs).
The random-effect variance is the mean random-effect
variance. Calculation is based on the proposal from Johnson et
al. 2014, which is also implemented in functions like performance::r2()
or insight::get_variance()
to get r-squared values or random effect variances for mixed models with
more complex random effects structures.
As can be seen, compared to the previous example with
type = "fixed", predicted values are identical (both on the
population-level). However, standard errors, and thus the resulting
confidence (or prediction) intervals are much larger .
pr <- ggpredict(m, "Days", type = "random")
pr
#> # Predicted values of Reaction
#>
#> Days | Predicted | 95% CI
#> -----------------------------------
#> 0 | 251.41 | [199.49, 303.32]
#> 1 | 261.87 | [209.98, 313.77]
#> 2 | 272.34 | [220.29, 324.39]
#> 3 | 282.81 | [230.42, 335.19]
#> 5 | 303.74 | [250.18, 357.30]
#> 6 | 314.21 | [259.82, 368.60]
#> 7 | 324.68 | [269.30, 380.05]
#> 9 | 345.61 | [287.84, 403.39]
#>
#> Adjusted for:
#> * Subject = 0 (population-level)
plot(pr)
The reason why both type = "fixed" and
type = "random" return predictions at population-level is
because ggpredict() returns predicted values of the
response at specific levels of given model predictors, which
are defined in the data frame that is passed to the
newdata-argument (of predict()). The data
frame requires data from all model terms, including random
effect terms. This again requires to choose certain levels or values
also for each random effect term, or to set those terms to zero or
NA (for population-level). Since there is no general rule,
which level(s) of random effect terms to choose in order to represent
the random effects structure in the data, using the population-level
seems the most clear and consistent approach.
To get predicted values for a specific level of the random effect
term, simply define this level in the
condition-argument.
ggpredict(m, "Days", type = "random", condition = c(Subject = 330))
#> # Predicted values of Reaction
#>
#> Days | Predicted | 95% CI
#> -----------------------------------
#> 0 | 275.10 | [223.18, 327.01]
#> 1 | 280.75 | [228.86, 332.64]
#> 2 | 286.40 | [234.35, 338.45]
#> 3 | 292.05 | [239.67, 344.44]
#> 5 | 303.36 | [249.80, 356.92]
#> 6 | 309.01 | [254.62, 363.40]
#> 7 | 314.67 | [259.29, 370.04]
#> 9 | 325.97 | [268.20, 383.75]Finally, it is possible to obtain predicted values by simulating from
the model, where predictions are based on simulate().
ggpredict(m, "Days", type = "sim")
#> # Predicted values of Reaction
#>
#> Days | Predicted | 95% CI
#> -----------------------------------
#> 0 | 251.45 | [201.32, 302.20]
#> 1 | 261.83 | [210.95, 311.47]
#> 2 | 272.60 | [222.98, 322.60]
#> 3 | 282.73 | [233.34, 332.09]
#> 5 | 303.84 | [253.91, 353.27]
#> 6 | 314.26 | [264.25, 364.30]
#> 7 | 324.60 | [274.16, 374.90]
#> 9 | 345.53 | [295.58, 395.76]
#>
#> Adjusted for:
#> * Subject = 0 (population-level)Marginal effects for zero-inflated mixed models
For zero-inflated mixed effects models, typically fitted with the glmmTMB or GLMMadaptive packages, predicted values can be conditioned on
- the fixed effects of the conditional model only
(
type = "fixed") - the fixed effects and zero-inflation component
(
type = "zero_inflated") - the fixed effects of the conditional model only (population-level),
taking the random-effect variances into account
(
type = "random") - the fixed effects and zero-inflation component (population-level),
taking the random-effect variances into account
(
type = "zi_random") - all model parameters (
type = "sim")
library(glmmTMB)
data(Salamanders)
m <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined,
family = truncated_poisson,
data = Salamanders
)Similar to mixed models without zero-inflation component,
type = "fixed" and type = "random" for
glmmTMB-models (with zero-inflation) both return
predictions on the population-level, where the latter option accounts
for the uncertainty of the random effects. In short,
predict(..., type = "link") is called (however, predicted
values are back-transformed to the response scale).
ggpredict(m, "spp")
#> # Predicted counts of count
#>
#> spp | Predicted | 95% CI
#> --------------------------------
#> GP | 0.94 | [0.62, 1.40]
#> PR | 0.56 | [0.30, 1.02]
#> DM | 1.17 | [0.80, 1.70]
#> EC-A | 0.77 | [0.48, 1.23]
#> EC-L | 1.79 | [1.25, 2.55]
#> DES-L | 1.71 | [1.20, 2.44]
#> DF | 0.98 | [0.67, 1.44]
#>
#> Adjusted for:
#> * mined = yes
#> * site = NA (population-level)
ggpredict(m, "spp", type = "random")
#> # Predicted counts of count
#>
#> spp | Predicted | 95% CI
#> ---------------------------------
#> GP | 0.94 | [0.13, 6.92]
#> PR | 0.56 | [0.07, 4.32]
#> DM | 1.17 | [0.16, 8.61]
#> EC-A | 0.77 | [0.10, 5.77]
#> EC-L | 1.79 | [0.24, 13.09]
#> DES-L | 1.71 | [0.23, 12.56]
#> DF | 0.98 | [0.13, 7.22]
#>
#> Adjusted for:
#> * mined = yes
#> * site = NA (population-level)For type = "zero_inflated", the predicted response value
is the expected value mu*(1-p) without
conditioning on random effects. Since the zero inflation and the
conditional model are working in “opposite directions”, a higher
expected value for the zero inflation means a lower response, but a
higher value for the conditional model means a higher response. While it
is possible to calculate predicted values with
predict(..., type = "response"), standard errors and
confidence intervals can not be derived directly from the
predict()-function. Thus, confidence intervals for
type = "zero_inflated" are based on quantiles of simulated
draws from a multivariate normal distribution (see also Brooks et
al. 2017, pp.391-392 for details).
ggpredict(m, "spp", type = "zero_inflated")
#> # Predicted counts of count
#>
#> spp | Predicted | 95% CI
#> --------------------------------
#> GP | 0.23 | [0.14, 0.32]
#> PR | 0.04 | [0.02, 0.06]
#> DM | 0.36 | [0.21, 0.50]
#> EC-A | 0.08 | [0.04, 0.11]
#> EC-L | 0.45 | [0.23, 0.66]
#> DES-L | 0.53 | [0.29, 0.76]
#> DF | 0.33 | [0.20, 0.45]
#>
#> Adjusted for:
#> * mined = yes
#> * site = NA (population-level)For type = "zi_random", the predicted response value is
the expected value mu*(1-p), accounting for the
random-effect variances. Intervals are calculated in the same way as for
type = "zero_inflated", except that the mean random effect
variance is considered and thus prediction intervals rather
than confidence intervals are returned.
ggpredict(m, "spp", type = "zi_random")
#> # Predicted counts of count
#>
#> spp | Predicted | 95% CI
#> --------------------------------
#> GP | 0.23 | [0.02, 2.23]
#> PR | 0.04 | [0.00, 0.41]
#> DM | 0.36 | [0.03, 3.50]
#> EC-A | 0.08 | [0.01, 0.79]
#> EC-L | 0.45 | [0.03, 4.71]
#> DES-L | 0.53 | [0.04, 5.38]
#> DF | 0.33 | [0.03, 3.17]
#>
#> Adjusted for:
#> * mined = yes
#> * site = NA (population-level)Finally, it is possible to obtain predicted values by simulating from
the model, where predictions are based on simulate() (see
Brooks et al. 2017, pp.392-393 for details). To achieve this,
use type = "simulate".
ggpredict(m, "spp", type = "simulate")
#> # Predicted counts of count
#>
#> spp | Predicted | 95% CI
#> --------------------------------
#> GP | 1.08 | [0.00, 4.11]
#> PR | 0.30 | [0.00, 2.34]
#> DM | 1.50 | [0.00, 5.21]
#> EC-A | 0.53 | [0.00, 3.08]
#> EC-L | 2.19 | [0.00, 7.10]
#> DES-L | 2.26 | [0.00, 7.08]
#> DF | 1.31 | [0.00, 4.60]
#>
#> Adjusted for:
#> * mined = yes
#> * site = NA (population-level)Marginal effects for each level of random effects
Marginal effects can also be calculated for each group level in mixed
models. Simply add the name of the related random effects term to the
terms-argument, and set type = "random".
In the following example, we fit a linear mixed model and first simply plot the marginal effects, not conditioned on random-effect variances.
library(sjlabelled)
data(efc)
efc$e15relat <- as_label(efc$e15relat)
m <- lmer(neg_c_7 ~ c12hour + c160age + c161sex + (1 | e15relat), data = efc)
me <- ggpredict(m, terms = "c12hour")
plot(me)
Changing the type to type = "random" still returns
population-level predictions by default. Recall that the major
difference between type = "fixed" and
type = "random" is the uncertainty in the variance
parameters. This leads to larger confidence intervals (i.e. prediction
intervals) for marginal effects with type = "random".
To compute marginal effects for each grouping level, add the related
random term to the terms-argument. In this case, prediction
intervals are calculated and marginal effects are conditioned on each
group level of the random effects.
Marginal effects, conditioned on random effects, can also be
calculated for specific levels only. Add the related values into
brackets after the variable name in the terms-argument.
me <- ggpredict(m, terms = c("c12hour", "e15relat [child,sibling]"), type = "random")
plot(me, ci = FALSE)
…and including prediction intervals…
plot(me)
The most complex plot in this scenario would be a term
(c12hour) at certain values of two other terms
(c161sex, c160age) for specific levels of
random effects (e15relat), so we have four variables in the
terms-argument.
me <- ggpredict(
m,
terms = c("c12hour", "c161sex", "c160age", "e15relat [child,sibling]"),
type = "random"
)
plot(me)
If the group factor has too many levels, you can also take a random
sample of all possible levels and plot the marginal effects for this
subsample of group levels. To do this, use
term = "<groupfactor> [sample=n]".
set.seed(123)
m <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
me <- ggpredict(m, terms = c("Days", "Subject [sample=7]"), type = "random")
plot(me)
You can also add the observed data points for each group using
add.data = TRUE.
plot(me, add.data = TRUE, ci = FALSE)
References
Brooks ME, Kristensen K, Benthem KJ van, Magnusson A, Berg CW, Nielsen A, et al. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal. 2017;9: 378–400.
Johnson PC, O’Hara RB. 2014. Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol Evol, 5: 944-946. (doi: 10.1111/2041-210X.12225)