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lmer vs INLA for variance components

November 24, 2018

Just for fun, I decided to compare the estimates from lmer and INLA for the variance components of an LMM (this isn’t really something that you would ordinarily do – comparing frequentist and bayesian approaches). The codes are below. A couple of plots are drawn, which show the distribution of the hyperparameters (in this case variances) from INLA, which are difficult to get from the frequentist framework (there’s a link to a presentation by Douglas Bates in the code, detailing why you might not want to do it [distribution is not symmetrical], and how you could do it… but it’s a lot of work).

Note that we’re comparing the precision (tau) rather than the variance or SD… SD = 1/sqrt(tau)

#
# Compare lmer and inla for LMM
# largely taken from Spatial and spatio-temporal bayesian models with R-INLA (Blangiardo & Cameletti, 2015), section 5.4.2
#
m <- 10000 # N obs
set.seed(1234)
x <- rnorm(m)
group <- sample(seq(1, 100), size = m, replace = TRUE)
# simulate random intercept
tau.ri <- .25
1/sqrt(tau.ri) #SD
set.seed(4567)
v <- rnorm(length(unique(group)), 0, sqrt(1/tau.ri))
# assign random intercept to individuals
vj <- v[group]
# simulate y
tau <- 3
1/sqrt(tau) #SD
set.seed(8910)
b0 <- 5
beta1 <- 2
y <- rnorm(m, b0 + beta1*x + vj, 1/sqrt(tau))
library(lme4)
mod <- lmer(y ~ x + (1|group))
summary(mod)
vc <- VarCorr(mod)
library(INLA)
form <- y ~ x + f(group, model = "iid", param = c(1, 5e-5))
imod <- inla(form, family = "gaussian",
data = data.frame(y = y, x = x, group = group))
summary(imod)
cbind(truth = c(tau, tau.ri), lmer = 1/c(attr(vc, "sc")^2, unlist(vc)), inla = imod$summary.hyperpar$`0.5quant`)
plot(imod,
plot.fixed.effects = F,
plot.lincomb = F,
plot.random.effects = F,
plot.predictor = F,
plot.prior = TRUE)
plot(imod$marginals.hyperpar$`Precision for the Gaussian observations`, type = "l")
# the equivalent of this for lmer is not easy to get at all. One would have to profile the deviance function (see http://lme4.r-forge.r-project.org/slides/2009-07-16-Munich/Precision-4.pdf)
lo <- imod$summary.hyperpar$`0.025quant`[1]
hi <- imod$summary.hyperpar$`0.975quant`[1]
dd <- imod$marginals.hyperpar$`Precision for the Gaussian observations`
dd <- dd[dd[,1] >= lo & dd[,1] <= hi, ]
dd <- rbind(dd, c(hi, 0))
dd <- rbind(dd, c(lo, 0))
polygon(dd, col = "blue")
plot(imod$marginals.hyperpar$`Precision for group`, type = "l")
lo <- imod$summary.hyperpar$`0.025quant`[2]
hi <- imod$summary.hyperpar$`0.975quant`[2]
dd <- imod$marginals.hyperpar$`Precision for group`
dd <- dd[dd[,1] >= lo & dd[,1] <= hi, ]
dd <- rbind(dd, c(hi, 0))
dd <- rbind(dd, c(lo, 0))
polygon(dd, col = "blue")

view raw
lmer_vs_inla_lmm.R
hosted with ❤ by GitHub

As you’d hope, the results come pretty close to each other and the truth:

cbind(truth = c(tau, tau.ri), lmer = 1/c(attr(vc, "sc")^2, unlist(vc)), inla = imod$summary.hyperpar$`0.5quant`)
      truth      lmer      inla
       3.00 2.9552444 2.9556383
group  0.25 0.2883351 0.2919622

Code on Github…

From → R

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