Author: Kevin Wright

# Example generalized linear mixed model analysis with different packages

#### Kevin Wright

    library(agridat)
    data(crowder.seeds)
    dat <- crowder.seeds

    libs(lattice)
    dotplot(germ/n~gen|extract, dat, main="crowder.seeds", xlab="gen")

The response variable on the vertical axis is the germination percent.

There are two predictive factors, `gen` (O73, O75) and `extract` (bean/cucumber).

Each dot is for a separate plate.

# GLM (not mixed–no random plate)

### brms

It takes a minute or so to compile Stan program…

Note, in Emacs brms ends the R process for some reason!

    if(require(brms)){
      m1.brms <- brms::brm( germ|trials(n)~ gen*extract,
         data = dat,
         family = binomial,
         chains=3, iter=3000, warmup=1000)
      summary(m1.brms)
      # round(  summary(m1.brms)$fixed[,1:4] , 2)
      #                        Estimate Est.Error l-95% CI u-95% CI
      # Intercept                 -0.42      0.18    -0.77    -0.06
      # genO75                    -0.14      0.22    -0.56     0.29
      # extractcucumber            0.55      0.25     0.07     1.05
      # genO75:extractcucumber     0.77      0.30     0.18     1.36
    }

### glm

    # ----- GLM.
    # family=binomial() fixes dispersion at 1
    # family=quasibinomial() estimates dispersion, had larger std errors
    m1.glm <- glm(cbind(germ,n-germ) ~ gen*extract,
                  data=dat,
                  #family="binomial",
                  family=quasibinomial()
                  )
    summary(m1.glm)
    ## round(summary(m1.glm)$coef,2)
    ##                        Estimate Std. Error t value Pr(>|t|)
    ## (Intercept)               -0.41       0.25   -1.64     0.12
    ## genO75                    -0.15       0.30   -0.48     0.64
    ## extractcucumber            0.54       0.34    1.58     0.13
    ## genO75:extractcucumber     0.78       0.42    1.86     0.08

### rstan

      # ----- Stan using pre-built models from rstanarm
      libs(tidyverse, rstan, rstanarm,bayesplot)
      set.seed(42)
      m1.stan <- stan_glm( cbind(germ,n-germ) ~ gen*extract,
                          data=dat,
                          family = binomial(link="logit")  )
      summary(m1.stan)
      ## round(posterior_interval(m1.stan, prob=.90),3)
      #                            5%    95%
      # (Intercept)            -0.728 -0.115
      # genO75                 -0.506  0.243
      # extractcucumber         0.133  0.947
      # genO75:extractcucumber  0.255  1.267

      libs(bayesplot)
      mcmc_areas(m1.stan, prob = 0.9) +
        ggtitle("Posterior distributions",
                "with medians and 95 pct intervals")

# Generalized Linear Mixed Model

### asreml

    if(require(asreml)){
      m1.asreml <- asreml(germ ~ gen*extract,
                          data=dat,
                          random= ~ plate,
                          family=asr_binomial(dispersion=1, total=n))
      summary(m1.asreml)
      ##
      ##                          effect
      ## (Intercept)               -0.47
      ## gen_O73                    0.00
      ## gen_O75                   -0.08
      ## extract_bean               0.00
      ## extract_cucumber           0.51
      ## gen_O73:extract_bean       0.00
      ## gen_O73:extract_cucumber   0.00
      ## gen_O75:extract_bean       0.00
      ## gen_O75:extract_cucumber   0.83

    }

### MASS::glmmPQL

    # --- GLMM.  Assumes Gaussian random effects
    libs(MASS)
    m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract,
                       random= ~1|plate,
                       family=binomial(), data=dat)
    summary(m1.glmm)
    ## round(summary(m1.glmm)$tTable,2)
    ##                        Value Std.Error DF t-value p-value
    ## (Intercept)            -0.44      0.25 17   -1.80    0.09
    ## genO75                 -0.10      0.31 17   -0.34    0.74
    ## extractcucumber         0.52      0.34 17    1.56    0.14
    ## genO75:extractcucumber  0.80      0.42 17    1.88    0.08

### glmmTMB

    libs(glmmTMB)
    m1.glmmtmb <- glmmTMB(cbind(germ, n-germ) ~ gen*extract + (1|plate),
                          data=dat,
                          family=binomial)
    round(summary(m1.glmmtmb)$coefficients$cond , 2)
    ##                        Estimate Std. Error z value Pr(>|z|)
    ## (Intercept)               -0.45       0.22   -2.03     0.04
    ## genO75                    -0.10       0.28   -0.35     0.73
    ## extractcucumber            0.53       0.30    1.74     0.08
    ## genO75:extractcucumber     0.81       0.38    2.11     0.04

### hglm

    # ----- HGML package. Beta-binomial with beta-distributed random effects
    if(require(hglm)){
      m1.hglm <- hglm(fixed= germ/n ~ I(gen=="O75")*extract, weights=n, data=dat,
                      random=~1|plate, family=binomial(), rand.family=Beta(),
                      fix.disp=1)
      summary(m1.hglm)
      # round(summary(m1.hglm)$FixCoefMat,2)
      ##                                     Estimate Std. Error t-value Pr(>|t|)
      ## (Intercept)                            -0.47       0.24   -1.92     0.08
      ## I(gen == "O75")TRUE                    -0.08       0.31   -0.25     0.81
      ## extractcucumber                         0.51       0.33    1.53     0.16
      ## I(gen == "O75")TRUE:extractcucumber     0.83       0.43    1.92     0.08
    }

### INLA

See: https://haakonbakka.bitbucket.io/btopic102.html

    if(require(INLA)){
      #gen,extract are fixed. plate is a random effect
      #Priors for hyper parameters. See: inla.doc("pc.prec")
      hyper1 = list(theta = list(prior="pc.prec", param=c(1,0.01)))
      m1.inla = inla(germ ~ gen*extract + f(plate, model="iid", hyper=hyper1),
                     data=crowder.seeds,
                     family="binomial", Ntrials=n,
                     control.family=list(control.link=list(model="logit")))
      round( summary(m1.inla)$fixed, 2)
      ##                         mean   sd 0.025quant 0.5quant 0.975quant  mode kld
      ## (Intercept)            -0.47 0.24      -0.96    -0.46       0.00 -0.46   0
      ## genO75                 -0.08 0.31      -0.68    -0.09       0.54 -0.09   0
      ## extractcucumber         0.53 0.33      -0.13     0.53       1.18  0.53   0
      ## genO75:extractcucumber  0.82 0.43      -0.01     0.82       1.69  0.82   0
    }

### rjags

Requires JAGS to be installed.

    # JAGS/BUGS.  See https://mathstat.helsinki.fi/openbugs/Examples/Seeds.html
    # Germination rate depends on p, which is a logit of a linear predictor
    # based on genotype and extract, plus random deviation to intercept

    # To match the output on the BUGS web page, use: dat$gen=="O73".
    # We use dat$gen=="O75" to compare with the parameterization above.

    jdat =list(germ = dat$germ, n = dat$n,
    root = as.numeric(dat$extract=="cucumber"),
    gen = as.numeric(dat$gen=="O75"),
    nobs = nrow(dat))

    jinit = list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10)

    # Use logical names (unlike BUGS documentation)
    mod.bug =
    "model {
      for(i in 1:nobs) {
        germ[i] ~ dbin(p[i], n[i])
        b[i] ~ dnorm(0.0, tau)
        logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] +
                       g75ecuke * gen[i] * root[i] + b[i]
      }
      int ~ dnorm(0.0, 1.0E-6)
      genO75 ~ dnorm(0.0, 1.0E-6)
      extcuke ~ dnorm(0.0, 1.0E-6)
      g75ecuke ~ dnorm(0.0, 1.0E-6)
      tau ~ dgamma(0.001, 0.001)
      sigma <- 1 / sqrt(tau)
    }"

    libs(rjags)
    oo <- textConnection(mod.bug)
    j1 <- jags.model(oo, data=jdat, inits=jinit, n.chains=1)
    close(oo)

    c1 <- coda.samples(j1, c("int","genO75","g75ecuke","extcuke","sigma"),
                       n.iter=20000)
    summary(c1) # Medians are very similar to estimates from hglm
    # libs(lucid)
    # print(vc(c1),3)
    ##             Mean    SD    2.5%  Median   97.5%
    ## extcuke   0.543  0.331 -0.118   0.542   1.2
    ## g75ecuke  0.807  0.436 -0.0586  0.802   1.7
    ## genO75   -0.0715 0.309 -0.665  -0.0806  0.581
    ## int      -0.479  0.241 -0.984  -0.473  -0.0299
    ## sigma     0.289  0.142  0.0505  0.279   0.596

    # Plot observed data with HPD intervals for germination probability
    c2 <- coda.samples(j1, c("p"), n.iter=20000)
    hpd <- HPDinterval(c2)[[1]]
    med <- summary(c2, quantiles=.5)$quantiles
    fit <- data.frame(med, hpd)

    libs(latticeExtra)
    obs <- dotplot(1:21 ~ germ/n, dat,
                   main="crowder.seeds", ylab="plate",
                   col=as.numeric(dat$gen), pch=substring(dat$extract,1))
    obs + segplot(1:21 ~ lower + upper, data=fit, centers=med)

### R2jags

    libs("agridat")
    libs("R2jags")
    dat <- crowder.seeds

    # To match the output on the BUGS web page, use: dat$gen=="O73".
    # We use dat$gen=="O75" to compare with the parameterization above.
    jdat =list(germ = dat$germ, n = dat$n,
               root = as.numeric(dat$extract=="cucumber"),
               gen = as.numeric(dat$gen=="O75"),
               nobs = nrow(dat))

    jinit = list(list(int = 0, genO75 = 0, extcuke = 0, g75ecuke = 0, tau = 10))

    mod.bug = function() {
      for(i in 1:nobs) {
        germ[i] ~ dbin(p[i], n[i])
        b[i] ~ dnorm(0.0, tau)
        logit(p[i]) <- int + genO75 * gen[i] + extcuke * root[i] +
          g75ecuke * gen[i] * root[i] + b[i]
      }
      int ~ dnorm(0.0, 1.0E-6)
      genO75 ~ dnorm(0.0, 1.0E-6)
      extcuke ~ dnorm(0.0, 1.0E-6)
      g75ecuke ~ dnorm(0.0, 1.0E-6)
      tau ~ dgamma(0.001, 0.001)
      sigma <- 1 / sqrt(tau)
    }

    parms <- c("int","genO75","g75ecuke","extcuke","sigma")

    j1 <- jags(data=jdat, inits=jinit, parms, model.file=mod.bug,
               n.iter=20000, n.chains=1)
    print(j1)
    ##          mu.vect sd.vect   2.5%    25%     50%     75%   97.5%
    ## extcuke    0.519   0.325 -0.140  0.325   0.531   0.728   1.158
    ## g75ecuke   0.834   0.429 -0.019  0.552   0.821   1.101   1.710
    ## genO75    -0.096   0.305 -0.670 -0.295  -0.115   0.089   0.552
    ## int       -0.461   0.236 -0.965 -0.603  -0.455  -0.312   0.016
    ## sigma      0.255   0.148  0.033  0.140   0.240   0.352   0.572
    ## deviance 103.319   7.489 90.019 98.010 102.770 108.689 117.288

    traceplot(as.mcmc(j1))
    densityplot(as.mcmc(j1))
    HPDinterval(as.mcmc(j1))

    }