# [R] fixed effects constant in mcmcsamp

Douglas Bates bates at stat.wisc.edu
Tue Aug 8 20:05:29 CEST 2006

```Thank you for the thorough report.

On 8/8/06, Daniel Farewell <farewelld at cardiff.ac.uk> wrote:
> I'm fitting a GLMM to some questionnaire data. The structure is J individuals,
> nested within I areas, all of whom answer the same K (ordinal) questions. The
> model I'm using is based on so-called continuation ratios, so that it can be
> fitted using the lme4 package.
>
> The lmer function fits the model just fine, but using mcmcsamp to judge the
> variability of the parameter estimates produces some strange results. The
> posterior sample is constant for the fixed effects, and the estimates of the
> variance components are way out in the tails of their posterior samples.
>
> The model I'm using says (for l = 1, ..., L - 1)
>
> logit P(X[ijk] = l | X[ijk] >= l, U[i], V[j], W[k]) = U[i] + V[j] + W[k] + a[l]
>
> where X[ijk] is the ordinal response to question k for individual j in area i.
> The U, V, and W are random effects and the a's are fixed effects. Here's a
> function to simulate data which mimics this setup (with a sequence of binary
> responses Y[ijkl] = 1 iff X[ijk] = l):
>
> sim <- function(n = c(10, 10, 10), sd = c(0.5, 2, 0.5), a = seq(-5, 1, 2)) {
>
>  u <- rnorm(n, 0, sd)
>  v <- rnorm(prod(n[1:2]), 0, sd)
>  w <- rnorm(n, 0, sd)
>
>  i <- factor(rep(1:n, each = prod(n[2:3])))
>  j <- factor(rep(1:prod(n[1:2]), each = n))
>  k <- factor(rep(1:n, prod(n[1:2])))
>
>  df <- NULL
>
>  for(l in 1:length(a)) {
>
>   y <- rbinom(length(i), 1, plogis(u[i] + v[j] + w[k] + a[l]))
>   df <- rbind(df, data.frame(i, j, k, l, y))
>
>   i <- i[!y]
>   j <- j[!y]
>   k <- k[!y]
>
>  }
>
>  df\$l <- factor(df\$l)
>  return(df)
>
> }
>
> And here's a function which shows the difficulties I've been having:
>
> test <- function(seed = 10, ...) {
>
>  require(lme4)
>  set.seed(seed)
>
>  df <- sim(...)
>  df.lmer <- lmer(y ~ 0 + l + (1 | i) + (1 | j) + (1 | k), family = binomial,
> data = df)
>  df.mcmc <- mcmcsamp(df.lmer, 1000, trans = FALSE)
>
>  print(summary(df.lmer))
>  print(summary(df.mcmc))
>  densityplot(~ as.numeric(df.mcmc) | factor(rep(colnames(df.mcmc), each =
> 1000)), scales = list(relation = "free"))
>
> }
>
> Running
>
> test()
>
> gives the following:
>
> Generalized linear mixed model fit using PQL
> Formula: y ~ 0 + l + (1 | i) + (1 | j) + (1 | k)
> Data: df
>       AIC      BIC    logLik deviance
>  2316.133 2359.166 -1151.066 2302.133
> Random effects:
>  Groups Name        Variance Std.Dev.
>  j      (Intercept) 4.15914  2.03940
>  k      (Intercept) 0.25587  0.50584
>  i      (Intercept) 0.56962  0.75473
> number of obs: 3455, groups: j, 100; k, 10; i, 10
>
> Estimated scale (compare to 1)  0.8747598
>
> Fixed effects:
>    Estimate Std. Error  z value  Pr(>|z|)
> l1 -4.50234    0.40697 -11.0632 < 2.2e-16 ***
> l2 -3.27643    0.38177  -8.5821 < 2.2e-16 ***
> l3 -1.05277    0.36566  -2.8791  0.003988 **
> l4  0.76538    0.36832   2.0780  0.037706 *
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Correlation of Fixed Effects:
>    l1    l2    l3
> l2 0.843
> l3 0.841 0.900
> l4 0.805 0.865 0.921
>        l1               l2               l3               l4
>  Min.   :-4.502   Min.   :-3.276   Min.   :-1.053   Min.   :0.7654
>  1st Qu.:-4.502   1st Qu.:-3.276   1st Qu.:-1.053   1st Qu.:0.7654
>  Median :-4.502   Median :-3.276   Median :-1.053   Median :0.7654
>  Mean   :-4.502   Mean   :-3.276   Mean   :-1.053   Mean   :0.7654
>  3rd Qu.:-4.502   3rd Qu.:-3.276   3rd Qu.:-1.053   3rd Qu.:0.7654
>  Max.   :-4.502   Max.   :-3.276   Max.   :-1.053   Max.   :0.7654
>      j.(In)          k.(In)           i.(In)           deviance
>  Min.   :1.911   Min.   :0.0509   Min.   :0.06223   Min.   :2011
>  1st Qu.:2.549   1st Qu.:0.1310   1st Qu.:0.19550   1st Qu.:2044
>  Median :2.789   Median :0.1756   Median :0.25581   Median :2054
>  Mean   :2.824   Mean   :0.2085   Mean   :0.29948   Mean   :2054
>  3rd Qu.:3.070   3rd Qu.:0.2463   3rd Qu.:0.34640   3rd Qu.:2064
>  Max.   :4.615   Max.   :0.8804   Max.   :3.62168   Max.   :2107
>
> As you can see, the posterior samples from the fixed effects are constant (at
> the inital estimates) and the estimates of the variance components aren't within
> the IQ ranges of their posterior samples.
>
> I understand from various posts that mcmcsamp is still a work in progress, and
> may not work on every model. Is this one of those cases? I'm using R 2.3.1 and
> lme4 0.995-2 on Windows XP.