## fitting
library(lme4)
library(glmmTMB)

## Warning in check_dep_version(dep_pkg = "TMB"): package version mismatch: 
## glmmTMB was built with TMB package version 1.9.17
## Current TMB package version is 1.9.18
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)

library(lattice)
## diagnostics etc.
library(broom.mixed)
library(DHARMa)
library(performance)
library(sjPlot)
library(dotwhisker)
##
library(ggplot2); theme_set(theme_bw())
library(cowplot)

• Most examples here taken from Vincent Zoonekynd’s page
• See also supplementary material from Bolker book chapter (source code here)

• Spaghetti plot; alternatively +facet_wrap(~Subject)

print((q0 <- ggplot(sleepstudy, aes(Days, Reaction, colour = Subject))
    + geom_point()) + geom_line())

(Alternatives; connect, don’t colour; suppress legend; separate individuals into separate facets … for scatterplots, try ggalt::geom_encircle(). See examples here as well.)

library(lme4)
## per-group fit (fixed)
lm1 <- lmList(Reaction~Days|Subject, data=sleepstudy)
## fixed effect
lm1B <- lm(Reaction~Days*Subject, data=sleepstudy)
## random intercept
lm2 <- lmer(Reaction~Days+(1|Subject), data=sleepstudy)
## random slopes
lm3 <- lmer(Reaction~Days+(1+Days|Subject), data=sleepstudy) 

Use random slopes when feasible, i.e. when some individuals are measured on more than 1 day (Schielzeth and Forstmeier 2009)

pp <- expand.grid(Days=0:9, Subject=levels(sleepstudy$Subject))
pp <- (pp                  
  |> cbind(pred_int = predict(lm2, newdata=pp))
  |> cbind(pred_int_slope = predict(lm3, newdata=pp))
  |> cbind(pred_fixed = predict(lm1B, newdata=pp))
  |> tidyr::pivot_longer(starts_with("pred"))
)

theme_set(theme_classic()+theme(legend.position="none"))
q0+geom_line(data=pp, aes(y=value)) + facet_wrap(~name)

• DHARMa::simulateResiduals()
• performance::check_model()

Use broom.mixed (in conjunction with dotwhisker::dwplot)

• use tidy directly for more control (?tidy.merMod)
• may want to specify effects="fixed", conf.int=TRUE, conf.method="profile" (for CIs on random effects parameters)

tt <- tidy(lm3, conf.int=TRUE, conf.method="profile")

dwplot(tt)

lattice::dotplot(ranef(lm3))

## $Subject

Or sjPlot::plot_model()

emmeans, effects, car::Anova(), drop1, etc. all work …

• what is the maximal model?
• Which effects vary within which groups?
• If effects don’t vary within groups, then we can’t estimate among-group variation in the effect
  • convenient
  • maybe less powerful (among-group variation is lumped into residual variation)
• e.g. female rats exposed to different doses of radiation, multiple pups per mother, multiple measurements per pup (labeled by time). Maximal model … ?

Maximal model often won’t actually work

e.g.

• Culcita (coral-reef) example: randomized-block design, so each treatment (none/crabs/shrimp/both) is repeated in every block; thus (treat|block) is maximal
• CBPP data: each herd is measured in every period, so in principle we could use (period|herd), not just (1|herd)

• what does (x|g) (or (1+x|g) really do?
• both intercept (baseline) and slope vary across groups
• estimates bivariate zero-centered distribution:

\[ (\textrm{intercept}, \textrm{slope}) = \textrm{MVN}\left(\boldsymbol 0, \left[ \begin{array}{cc} \sigma^2_{\textrm{int}} & \sigma_{\textrm{int},\textrm{slope}} \\ \sigma_{\textrm{int},\textrm{slope}} & \sigma^2_{\textrm{slope}} \end{array} \right] \right) \]

equatiomatic::extract_eq(lm3)

\[ \begin{aligned} \operatorname{Reaction}_{i} &\sim N \left(\alpha_{j[i]} + \beta_{1j[i]}(\operatorname{Days}), \sigma^2 \right) \\ \left( \begin{array}{c} \begin{aligned} &\alpha_{j} \\ &\beta_{1j} \end{aligned} \end{array} \right) &\sim N \left( \left( \begin{array}{c} \begin{aligned} &\mu_{\alpha_{j}} \\ &\mu_{\beta_{1j}} \end{aligned} \end{array} \right) , \left( \begin{array}{cc} \sigma^2_{\alpha_{j}} & \rho_{\alpha_{j}\beta_{1j}} \\ \rho_{\beta_{1j}\alpha_{j}} & \sigma^2_{\beta_{1j}} \end{array} \right) \right) \text{, for Subject j = 1,} \dots \text{,J} \end{aligned} \]

• Avoid singular fits
• singular = zero variances, +/- 1 correlations
• More subtle for larger models: use isSingular()

• remove random effects
• simplify random effects (Bates et al. 2015; Matuschek et al. 2017; Barr et al. 2013)
  • drop slopes
  • force correlations to zero (use 1+x||g to fit intercept and slope independently)
  • structured random effects (diagonal, compound symmetric …)

• convergence failures are common
• what do they really mean? how to fix them? when can they be ignored?
• approximate test that gradient=0 and curvature is correct
• scale and center predictors; simplify model
• use ?allFit to see whether different optimizers give sufficiently similar answers
  • $fixef, etc.: are answers sufficiently similar?
  • $llik: how similar is goodness-of-fit?

m1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
aa <- allFit(m1)

## bobyqa : [OK]
## Nelder_Mead : [OK]
## nlminbwrap : [OK]
## nmkbw : [OK]
## optimx.L-BFGS-B : [OK]
## nloptwrap.NLOPT_LN_NELDERMEAD : [OK]
## nloptwrap.NLOPT_LN_BOBYQA : [OK]

ss <- summary(aa)
names(ss)

## [1] "which.OK" "msgs"     "fixef"    "llik"     "sdcor"    "theta"    "times"   
## [8] "feval"

ss$fixef

##                               (Intercept)     Days
## bobyqa                           251.4051 10.46729
## Nelder_Mead                      251.4051 10.46729
## nlminbwrap                       251.4051 10.46729
## nmkbw                            251.4051 10.46729
## optimx.L-BFGS-B                  251.4051 10.46729
## nloptwrap.NLOPT_LN_NELDERMEAD    251.4051 10.46729
## nloptwrap.NLOPT_LN_BOBYQA        251.4051 10.46729

source("https://raw.githubusercontent.com/lme4/lme4/refs/heads/master/misc/plot_allFit.R")
par(las=1); tpar(fmar = c(1,12,1,1))
plot(aa, flip = TRUE)

• PQL, Laplace, adaptive Gauss-Hermite quadrature (AGHQ)
• fast/flexible/inaccurate \(\to\) slow/inflexible/accurate
• amount of data per group is what matters
  • number of points, info per point (binary vs count)
• set nAGQ= to a larger value (1=Laplace; 25 should be more than enough)

• glmer pretty reliable
• glmmTMB more flexible, faster on extended models (NB etc.)

• Mixed Models task view
• the GLMM FAQ