# Comparing models fit in ubms and JAGS

#### Ken Kellner

# 1 Introduction

One of the key features of `ubms` is the ability to include random effects in model formulas. This is not possible in `unmarked`, but it is possible with custom models using JAGS. To explore how estimates of parameters and uncertainty intervals compared between `ubms` and `JAGS`, I simulated a basic occupancy dataset with known parameter values and fit models with both. In this simulation, individual sites were nested within groups, and I estimated group-level random intercepts. This is a common scenario for so-called stacked data from multiple years, where the “groups” are unique sites and “sites” represent unique site-year combinations.

# 2 Simulating the data

First I set the true parameter values for the vector of fixed effects \(\boldsymbol{\beta}\) and the random effects standard deviation \(\sigma\).

    beta <- c(0.4, -0.5, 0.3, 0.5)
    sigma <- 1.2

Then, I simulated covariate data for the occupancy and detection submodels. This included a random group assignment (26 possible groups) for each site.

    dat_occ <- data.frame(x1=rnorm(500), group=factor(sample(letters[1:26], 500, replace=T)))
    dat_p <- data.frame(x2=rnorm(500*5))

Next I simulated the random effect value for each group (with an effects parameterization centered on 0).

    re <- rnorm(26, 0, sigma)

Finally, I simulated an occupancy dataset `y` using the data and parameter values obtained above.

    y <- matrix(NA, 500, 5)
    z <- rep(NA, 500)
    group_idx <- as.numeric(dat_occ$group) #convert group assignment to numeric index
    idx <- 1
    for (i in 1:500){
      #True latent state of site i in group group_idx[i]
      z[i] <- rbinom(1,1, plogis(beta[1] + beta[2]*dat_occ$x1[i] + re[group_idx[i]]))

      #Observation process
      for (j in 1:5){
        #Observed data at site i for observation j
        y[i,j] <- z[i]*rbinom(1,1, plogis(beta[3] + beta[4]*dat_p$x2[idx]))
        idx <- idx + 1
      }
    }

# 3 Fitting the model in ubms

I combined the covariates and simulated data into an `unmarkedFrame`.

    library(ubms)
    umf <- unmarkedFrameOccu(y=y, siteCovs=dat_occ, obsCovs=dat_p)

Using `stan_occu`, I fit a model with a random effect of group on occupancy (`(1|group)`). I probably did not use enough iterations but it’s fine for this example.

    ubms_fit <- stan_occu(~x2~x1+(1|group), umf, chains=3, iter=300, cores=3, seed=123)

# 4 Fitting the model in JAGS

A bit more work is required to fit this model in JAGS. First I reorganized the data into a list so it could be sent to JAGS.

    nsites <- nrow(umf@y)
    jags_data <- list(y=umf@y, x1=dat_occ$x1, group=as.numeric(dat_occ$group),
                      ngroups=nlevels(dat_occ$group),
                      x2=matrix(dat_p$x2, nrow=nsites, byrow=TRUE),
                      nsites=nsites, nobs=ncol(umf@y))

Next I wrote a simple occupancy model with group-level intercepts `gint` in the BUGS language, and saved it to a temporary file.

    modfile <- tempfile()
    writeLines("
    model{

    #Likelihood
    for (i in 1:ngroups){
      gint[i] ~ dnorm(beta[1], tau.group)
    }

    for (i in 1:nsites){
      z[i] ~ dbern(psi[i])
      logit(psi[i]) <- gint[group[i]] + beta[2]*x1[i]

      for (j in 1:nobs){
        y[i,j] ~ dbern(p[i,j]*z[i])
        logit(p[i,j]) <- beta[3] + beta[4]*x2[i,j]
      }
    }

    #Priors
    for (i in 1:4){
      beta[i] ~ dnorm(0,0.01)
    }

    sd.group ~ dunif(0,10)
    tau.group <- pow(sd.group, -2)

    }
    ", con=modfile)

JAGS also requires a list of parameters you want to save, and a function to generate initial values. Generally you need to provide reasonable initial values for the latent state \(z\). Note that I’m saving both the parameter values (`"beta"` and `"sd.group"`), along with the actual random effects estimates (`"gint"`).

    params <- c("beta", "sd.group", "gint")
    inits <- function(){list(z=apply(y, 1, max, na.rm=TRUE))}

Finally, I fit the model using the R package jagsUI.

    set.seed(123)
    library(jagsUI)
    jags_fit <- jags(jags_data, inits, params, modfile, n.chains=3, n.adapt=100, n.iter=2000,
                     n.burnin=1000, n.thin=2, parallel=TRUE, verbose=FALSE)

Running the model in JAGS is significantly slower, although you could speed up by marginalizing the likelihood.

# 5 Compare fixed effect estimates

With the model fit in both `ubms` and `JAGS`, I compared the fixed effect parameter point estimates and uncertainty intervals visually.

    #Data frame of JAGS parameter estimates and UIs
    jags_sum <- jags_fit$summary
    beta_jags <- as.data.frame(jags_sum[1:5,c(1,3,7)])
    names(beta_jags) <- c("mean","lower","upper")
    beta_jags$source <- "JAGS"

    #Data frame of ubms parameter estimates and UIs
    ubms_sum <- summary(ubms_fit, "state")
    beta_ubms <- rbind(summary(ubms_fit, "state")[,c(1,4,8)],
                       summary(ubms_fit, "det")[,c(1,4,8)])
    beta_ubms <- beta_ubms[c(1:2,4:5,3),]
    names(beta_ubms) <- c("mean","lower","upper")
    beta_ubms$source <- "ubms"

    #Data frame of true parameter values
    beta_truth <- data.frame(mean=c(beta, sigma), lower=NA, upper=NA, source="Truth")

    #Bind together
    beta_plot <- rbind(beta_jags, beta_ubms, beta_truth)
    beta_plot$Parameter <- rep(rownames(beta_jags), 3)

    #Plot
    library(ggplot2)
    dodge <- position_dodge(0.4)
    ggplot(data=beta_plot, aes(x=Parameter, y=mean, col=source)) +
      geom_errorbar(aes(ymin=lower, ymax=upper), position=dodge) +
      geom_point(position=dodge, size=3) +
      labs(x='Parameter',y='Value', col='Source') +
      theme_bw() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
            axis.text=element_text(size=12), axis.title=element_text(size=14),
            legend.text=element_text(size=12), legend.title=element_text(size=14))

Fixed-effects parameter estimates are very similar between `ubms` and `JAGS`, as are the uncertainty intervals. In both cases the intervals overlap the true parameter values.

# 6 Compare random effect estimates

Finally, I did the same for the random effect estimates. The `ranef` method extracts random effects terms from a fitted `ubms` model. Although we specified the random effect in `ubms` as an “effects” parameterization, `ranef` automatically adds the random effect to the intercept term to get the complete random intercept at the group level. The group-level random intercepts in JAGS are parameter `"gint"`.

    #Get random effect estimates from ubms
    re_ubms <- ranef(ubms_fit, "state", summary=TRUE)[[1]][[1]][,c(1,3,4)]
    re_ubms$source <- "ubms"

    #Get random effects estimates from JAGS
    re_jags <- as.data.frame(jags_sum[grepl("gint", rownames(jags_sum)),c("mean","2.5%","97.5%")])
    re_jags$source <- "JAGS"
    names(re_jags) <- names(re_ubms)

    #Get truth
    truth <- data.frame(Estimate=re, `2.5%`=NA, `97.5%`=NA, source="Truth", check.names=FALSE)

    #Combine
    plot_dat <- rbind(re_ubms, re_jags, truth)
    plot_dat$group <- rep(letters[1:26], 3)
    levels(plot_dat$source) <- c("Truth","JAGS","ubms")

    library(ggplot2)
    dodge <- position_dodge(0.4)

    #Plot a subset of random effects
    plot_dat_sub <- plot_dat[plot_dat$group %in% letters[1:6],]
    ggplot(data=plot_dat_sub, aes(x=group, y=Estimate, col=source)) +
      geom_errorbar(aes(ymin=`2.5%`, ymax=`97.5%`), position=dodge) +
      geom_point(position=dodge, size=3) +
      labs(x='Group',y='Random effect value', col='Source') +
      theme_bw() +
      theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
            axis.text=element_text(size=12), axis.title=element_text(size=14),
            legend.text=element_text(size=12), legend.title=element_text(size=14))

As with the fixed effects, the estimated random intercepts are very similar between the two methods. Success!