Binomial data are frequently encountered in the fields of ecology and evolution. Researchers often wish to know what factors determine the proportion of offspring sired by a focal individual (Tyler et al., 2013), the proportion of eggs of a clutch that successfully hatch (Harrison et al., 2013a), or the prevalence of disease in a population (Bielby et al., 2014). To determine which factors drive variation in the proportion data of interest, researchers often fit Binomial models to their data and model the Binomial mean as a function of covariates. However, in many cases these Binomial models exhibit overdispersion, where the variance of the data is greater than that predicted by the model (e.g., Zuur et al., 2009; Bolker et al., 2009). Failing to deal with overdispersion can lead to biased parameter estimates and standard errors in these models (Hilbe, 2011; Harrison, 2014), potentially leading to false conclusions regarding which covariates are truly influential on the outcome variable. It is therefore crucial that we find robust means to deal with overdispersion in order to correctly identify the biological processes underlying our observed Binomial data.
Several methods to deal with overdispersion are currently available. As overdispersion can downwardly bias standard errors in models, one method involves ‘correcting’ the standard errors by multiplying them by the square root of the dispersion coefficient (Zuur et al., 2009). This multiplicative correction for overdispersion occurs when one specifies the ‘quasi’ family in Generalized Linear Models (GLMs) in the statistical software R (R Core Team, 2014). However, a weakness of the ‘quasi’ approach is that it does not model the overdispersion in the data, but merely adjusts the resulting parameter estimates with a single correction factor. The assumption that all standard errors are biased to the same degree is an obvious problem, which may not be appropriate (e.g., Harrison, 2014, Table 1). The alternatives to the ‘quasi’ approach for proportion data are to explicitly model the source of extra-Binomial variation in the data (e.g., Williams, 1982; Hughes & Madden, 1993; Lee & Nelder, 1996; Richards, 2008), for example by using compound probability structures (e.g., Beta-Binomial models), or to use observation-level random effects (OLRE). With OLRE models, each observation in the model receives a unique level of a random effect that absorbs the extra-Binomial variation in the data, hopefully yielding a model with unbiased parameter estimates and without overdispersion. However, although several studies have sought to investigate the utility of OLRE to model overdispersion in Poisson count data (Elston et al., 2001; Harrison, 2014), similar investigations for Binomial proportion data are relatively rare. Harrison (2014) found that for Poisson data, OLRE yielded accurate parameter estimates and r2 values in most situations of overdispersion, but that OLRE could not adequately cope with overdispersion caused by zero-inflation. Here I will address the shortfall in our understanding of the capacity of OLRE to model overdispersion in Binomial data, with a specific focus on mixed effects models. In order for OLRE to be an appropriate tool, they should be robust to the process generating overdispersion in the data, and thus I test OLRE on overdispersed Binomial data generated by a variety of mechanisms. In addition, I explore the utility of Beta-Binomial hierarchical models as an alternative to OLRE models, and compare the accuracy of parameter estimates derived from both approaches.
A typical Binomial example
For these examples, I will assume the outcome variable that we are measuring is the number of eggs h that have hatched out of a total clutch c for a hypothetical lizard species. I assume that the proportion of hatched eggs is described by a Binomial distribution: (1) where hi is the number of eggs hatched by individual i from its total clutch ci, with mean probability pi. In a typical Binomial model, we can model the mean hatch rate pi as a function of covariates of interest. Let us assume that hatch rate shows a positive relationship with how many prey items an individual lizard has consumed, and also that there is a weak negative relationship between body size and hatch rate. Let us also assume we have measured N individuals from J populations of lizards, and that we wish to control for variation among populations using a random intercept. (2) (3) (4) where the hatch rate pi is a function of a linear model. alphaj(i) is the intercept for population j to which individual i belongs, where each alphaj is drawn from a normal distribution with mean μpop and variance (Eq. (3)). βprey and βbodysize are the slope parameters for the effects of number of prey items consumed and body size, respectively. Preyi and Bodysizei are the prey and body size measurements of individual i. We convert the linear predictor (logit.pi from, Eq. (1)) back to a probability (pi) using a logit link (Eq. (4)). Suppose now we wanted to model our hatching success data using these covariates. Statistical packages such as lme4 (Bates et al., 2014) readily fit such generalized linear mixed models (GLMMs)
m1<-glmer(cbind(hatch,clutch-hatch) ~ Prey + Bodysize + (1|Population),family=binomial(logit)) R Code
where hatch and clutch are vectors where each row corresponds to the measurements for a single individual i for hi and ci from Eq. (1). Prey and Bodysize are vectors of measurements of Preyi and Bodysizei corresponding to Eq. (2). Population is a vector denoting the population ID of each individual. We also specify the Binomial error distribution with a logit link using the ‘family’ argument in the glmer call. When we fit model m1, we are modeling our data according to Eq. (1)–Eq. (4) above. The model will estimate βprey, βbodysize, μpop, and σpop. In order to be confident that the resulting parameter estimates are robust, we should check for overdispersion in model m1. Bolker et al. (2009) and Harrison (2014) provide R code to calculate the dispersion parameter for such models. Briefly, a point estimate of the dispersion parameter can be calculated as the ratio of the sum of squared Pearson residuals to the residual degrees of freedom for the model, where a value >1 indicates overdispersion (Zuur et al., 2009; Bolker et al., 2009). Harrison (2014) also provides code to estimate the dispersion parameter and 95% confidence intervals using parametric bootstrapping. If the data exhibit overdispersion, we can adjust our model to take this into account, either with observation-level random effects, or using a compound error structure such as the Beta-Binomial.
Modeling overdispersion using observation-level random effects
Including an observation-level random effect requires that we modify Eq. (2) above to include an additional term in the linear predictor: (5) (6) where εi is an additional term unique to each observation i that is drawn from a normal distribution with a mean of 0 and variance . If a dataframe D containing the observations has N rows, we can create an observation level random effect as follows:
obs<-seq(nrow(D)) R Code
where D is the dataframe in which the values of Prey and Bodysize are stored. We can then modify our model m1 to include the OLRE denoted by ‘obs’:
M2<-glmer(cbind(hatch,clutch-hatch) ~ Prey + Bodysize + (1|Population + (1|obs),family=binomial(logit)) R Code
Model m2 will estimate the same parameters as m1, but in addition will also estimate the additional parameter . The larger the value of , the greater the degree of overdispersion in the dataset. The magnitude of the variance parameter can be informative, for example when compared to hierarchical variance components (e.g., individual nested within brood, nested within site (see Elston et al., 2001)). However, in many cases the OLRE will simply ‘soak up’ the extra-Binomial variation in the data, effectively treating the overdispersion as nuisance variation. The problem with this approach is that often the overdispersion might be biologically interesting (Zuur et al., 2009) and indeed relevant to our hypotheses regarding the processes underlying variation in the observed data. An alternative way to model overdispersion is by using hierarchical models such as Beta-Binomial models.
Modeling overdispersion using hierarchical Beta-Binomial models
An alternative to adding an observation-level random effect to models involves modelling the overdispersion using compound probability distributions such as the Beta-Binomial. The benefit of this approach is that by quantifying the process generating the overdispersion (through the estimate of ϕ, see below), one may gain a more precise understanding of the ecological mechanisms underlying observed data (Martin et al., 2005; Richards, 2008). For Beta-Binomial models, the linear predictor remains the same as Eq. (2)–Eq. (4) above, on the logit scale but instead of drawing observed counts directly from a Binomial distribution with mean pi, we draw the Binomial probabilities from a beta distribution with parameters a and b: (7) (8) (9) (10) where ai and bi are the shape and scale parameters of the Beta distribution for individual i, calculated using the value of pi (Eq. (4)) and ϕ, which is the constant overdispersion term in the model. As with for the OLRE models above, the larger the value of ϕ the greater the degree of overdispersion in the data.
Overdispersion and sample size
Multiple features biological data can influence the accuracy with which models recover parameter estimates for effect sizes. Overdispersion is likely a ubiquitous feature of the kinds of ‘messy’ data collected by ecologists from field and laboratory studies, and is known to bias parameter estimates in count data (Hilbe, 2011; Harrison, 2014). In addition, overdispersion can arise for a variety of reasons, including aggregation (heterogeneity) in the data, or through failing to measure important covariates, or include relevant interactions between covariates in models (Hilbe, 2011). However, multiple factors may interact with overdispersion to add further bias to models of overdispersed data, including most notably the sample size of the datasets. To date, relatively little is known in the ecological literature about the interaction between overdispersion and sample size and how this affects parameter estimates. This is particularly relevant to mixed effects models, where the number of grouping levels of a random intercept term (e.g., number of populations) can greatly influence model accuracy. Low replication at the level of the random effect grouping variable can mean there is not enough information to estimate the variance among groups, especially if one employs 5 or fewer levels (Gelman & Hill, 2006, p. 247). Unfortunately, ecologists often fit factors containing fewer than 5 levels as random effects (e.g., a random intercept for ‘Year,’ Harrison et al., 2013a; Harrison et al., 2013b), largely because gathering 3 or 4 years of data in the laboratory or field represents an enormous amount of work.
In order for OLRE to be considered a robust tool for modeling overdispersion in Binomial data, they should yield accurate parameter estimates under a broad range of conditions, including high overdispersion and low sample size. This paper investigates the influence of 3 specific variables on the accuracy of parameter estimates from mixed models: (i) for a fixed sample size, the influence of weak, moderate and strong overdispersion; (ii) for strong overdispersion, the influence of the level of replication of the random intercept term; and (iii) for strong overdispersion, the influence of Binomial sample size. For all three scenarios, I consider overdispersion resulting from two mechanisms, using either a Beta-Binomial distribution or an overdispersed Binomial distribution to generate the data (see equations above). Finally, I use both OLRE and Beta-Binomial models to assess the relative performance of each model type for a given scenario of overdispersion and sample size. Model performance is assessed by (i) quantifying the accuracy with which the models can recover estimates of βprey, μpop and (values fixed for all simulations); and (ii) including a weak negative effect of βbodysize (−0.01), corresponding to roughly a 4% difference in reproductive success between the smallest and largest individuals in the dataset, and quantifying the proportion of simulation replicates for a given scenario that incorrectly inferred a positive slope for βbodysize. Such an outcome is important, because most ecological datasets likely contain variables of weak effect that are ‘biologically relevant’ to the organism(s) in question, but our ability to detect such effects in the presence of overdispersion has received relatively little attention (but see Richards, 2008).
I explored the consequences varying three key parameters in Binomial mixed models: (i) the magnitude of overdispersion (‘Overdispersion’ Scenario), (ii) the number of the levels of the random intercept term, (‘Random Effect’ Scenario) and (iii) the Binomial sample size (number of trials per observation) (‘Binomial Sample Size’ Scenario). For each scenario, I simulated data from both an overdispersed Binomial distribution using Eq. (1)–Eq. (6) and a Beta-Binomial distribution using Eq. (1)–Eq. (4) and Eq. (7)–Eq. (10) (see Introduction) to examine whether the accuracy of the mixed models also depended on the mechanism generating the overdispersion in the data. For the Overdispersion simulations, ϕ was set at 0.1, 1, or 2 for the Beta-Binomial data, and σε (specified as standard deviation, not variance , in the R code) set to 0.1, 1.5 or 3 for the corresponding overdispersed Binomial data. A value of σε = ϕ = 0.1 corresponds to weak overdispersion (model dispersion parameter ∼1.1), whereas σε = 3/ϕ = 2 corresponds to a model dispersion parameter of ∼2. For all simulations parameter values were fixed at the following: μpop = − 1; σpop = 0.5; βbodysize = − 0.01; βprey = 0.6. For the Overdispersion and Binomial Sample Size scenarios, I assumed 10 different populations had been sampled, each with a sample size of 20 individuals (n = 200). For the Random Effect scenario, the number of populations was set at 3, 5 or 20. Clutch size (Ci) was fixed at 5 for the Overdispersion and Random Effect simulations, but was set to 2, 4 or 10 for the Binomial Sample Size scenario. Full details of the parameters used in each of the 3 scenarios are provided in Table 1.
|Overdispersion ϕ/σε||n trials (clutch size)||n individuals||n populations|
|Levels of random effect|
|Binomial sample size|
Model fitting simulations
All simulations were coded in R v3.1.1 (R Core Team, 2014). One thousand datasets were simulated for each set of three different parameter estimates for each of the three scenarios and data types (Beta-Binomial or overdispersed Binomial, see Table 1). For each dataset, I fitted model m2 containing an OLRE (see Introduction) in the lme4 package and extracted parameter estimates for μpop, σpop, βprey, βbodysize and σε (the SD of the observation level random effect, ‘obs’). Following 1,000 simulations, I calculated simulation means and 95% quantiles for parameters. I also calculated the proportion of models that falsely estimated the effect of body size to be positive (βprey > 0). Data for the proportion of models where βprey > 0 are presented as means and bootstrapped 95% confidence intervals for each parameter/data type combination. I did not test for significant differences between mean values for each parameter.
For each of the three scenarios and two data types, I fitted a corresponding Bayesian Beta-Binomial hierarchical model in JAGS (Plummer, 2013) using the R package runjags (Denwood, 2014), following Eq. (1)–Eq. (4) and Eq. (7)–Eq. (10) above. This resulted in four combinations of data-generating process and statistical model used in analysis: Beta-Binomial data with OLRE, overdispersed Binomial data with OLRE, Beta-Binomial data with a Beta-Binomial Model, and overdispersed Binomial data with Beta-Binomial model. The Bayesian framework is extremely flexible, meaning models following these equations can be easily specified, even though few frequentist mixed model packages in R permit the fitting of Beta-Binomial models (but see spaMM, Rousset & Ferdy, 2014; glmmADMB, Fournier et al., 2012). Models were run for 20,000 iterations with a thinning interval of 20 following a burnin of 2,000. Convergence was assessed by running two parallel chains and calculating the Gelman–Rubin statistic, which was below 1.05 for parameters, indicating convergence. Results are presented as posterior means and 95% credible intervals for all parameters. I used uninformative Normal priors with mean 0 and precision 0.001 for μpop, βprey and βbodysize; an uninformative uniform prior on the interval (0,10) for σpop, and an uninformative gamma prior with a = b = 0.001 for ϕ. To test sensitivity of model output to choice of priors, I reran models where ϕ had a uniform prior on the interval (0,10) and σpop had a gamma prior where a = b = 0.001. Results from both sets of models were similar, suggesting limited sensitivity to prior specification.
Unlike frequentist models, their Bayesian equivalents are much more computationally intensive and thus slower to run. Because of this, I generated only 1 dataset and ran 1 model for each scenario/data type combination (n = 18). Both Bayesian and frequentist data are plotted alongside one another in the figures, but it is important to note that the frequentist data are the 95% intervals of the distribution of 1,000 means for each parameter, whilst the Bayesian data are the 95% credible intervals of 1,000 samples from the parameter space for a single mean. Although they are slightly different quantities, the point of the comparison is to assess the relative accuracy of a Beta-Binomial model compared to a Binomial model allowing for overdispersion on the linear predictor i.e., containing observation-level random effects. However because of the use of Bayesian analyses in these simulations, the type of model (OLRE or Beta-Binomial) is therefore confounded with the fitting algorithm (Maximum Likelihood or Bayesian, respectively). That is, frequentist methods may perform poorly in generalized mixed models (Ferkingstad & Rue, 2015), and Bayesian methods may perform slightly better, and this may have little to do with the type of model. To test the sensitivity of the parameter estimates to Beta-Binomial modeling philosophy, I reran the data simulations for highly overdispersed Binomial (σε = 3) and Beta-Binomial (ϕ = 2) data with 10 populations, 20 individuals per population and a clutch size of 5 per individual. Instead of Bayesian Beta-Binomial models, I fitted frequentist Beta-Binomial mixed models using the ‘spaMM’ package’ and extracted means and 95% confidence intervals for parameters after 1,000 simulations.
Model code for the Bayesian models, and all data simulations in the manuscript are provided in Online Supplementary Information.
Weak overdispersion (σε/ϕ = 0.1) resulted in accurate parameter estimates for βprey, μpop and σpop for all four data/models combinations as expected (Fig. 1). However, for both moderate and strong overdispersion, bias increased for all parameters when the data were generated from a Beta-Binomial distribution but analysed using OLRE (yellow circles, Fig. 1). Conversely, the overdispersed Binomial/OLRE model did not suffer the same bias, although the standard error of all estimates increased in tandem with overdispersion (blue circles, Fig. 1). Beta-Binomial models performed well for both Beta-Binomial and overdispersed Binomial data (yellow and blue diamonds, Fig. 1), but were unable to accurately estimate σpop when overdispersion was high (σε/ϕ = 3/2 respectively). Increasing overdispersion caused an increase in the proportion of models incorrectly inferring a positive slope for βbodysize for both types of data (Fig. 4A). Summary: OLRE are highly sensitive to the mechanism generating the overdispersion in the data, yielding large bias when applied to Beta-Binomial data. Parameter bias gets progressively worse as overdispersion increases.
Number of levels of the random effect
For all four data/model combinations, the precision of the estimates increased as the number of levels of the random effect increased from 3 to 20 (Fig. 2). This was expected, as a higher number of levels yields more information to estimate hierarchical modeling components such as σpop. However, for the Beta-Binomial data/OLRE model, increasing the number of levels yielded both a consistently biased mean value for βprey, and increased precision around the biased mean (yellow circles, Fig. 2). OLRE models on overdispersed Binomial data (blue circles) performed generally well for both βprey and μpop. Conversely a Beta-Binomial model on overdispersed Binomial data (blue diamonds, Fig. 2) tended to underestimate βprey. For all four data/model combinations, estimates of σpop were highly imprecise when only 3 populations were considered. This was especially true for both Beta-Binomial models (yellow and blue diamonds, Fig. 2). There was still a large degree of bias when n = 5, especially when OLRE models were applied to Beta-Binomial data (yellow circles and diamonds). The proportion of models recovering βbodysize > 0 tended to decrease as the n increased, but only by 5% on average, and there appeared to be no differences in proportion depending on the kind of overdispersion generated in the data. Increasing the sample size to ∼200 but using only 3 populations (67 individuals per population) still resulted in biased parameter estimates, especially for Beta-Binomial models (Table 2A), suggesting it is random effects sample size and not total sample size driving this pattern. Summary: Higher replication of the random effects results in more precise estimates, but cannot fix the bias caused by overdispersion when using OLRE for some data types. All models perform poorly when the number of populations is 3, especially for σpop, suggesting there is no modeling ‘fix’ for poor replication of the random effect.
|Beta-Binomial data||Overdispersed binomial data|
|Parameter||Mean||95% CI||Mean||95% CI|
|βprey (0.6)||2.2||[1.33, 3.41]||0.61||[0.19, 1.07]|
|βbodysize (−0.01)||−0.03||[−0.34, 0.29]||−0.007||[−0.18, 0.17]|
|μpop (−1)||−3.82||[−11.79, 2.99]||−1.06||[−4.82, 2.59]|
|σpop (0.5)||0.79||[0, 2.77]||0.23||[0, 0.89]|
|βprey (0.6)||0.62||[0.37, 0.89]||0.299||[0.02, 0.599]|
|βbodysize (−0.01)||−0.01||[−0.1, 0.08]||−0.004||[−0.09, 0.07]|
|μpop (−1)||−1.24||[−1.82, −0.69]||−0.59||[−1.24, −0.02]|
|σpop (0.5)||0.29||[0.14, 0.43]||0.18||[0.06, 0.29]|
slope parameter for effect of number of prey items consumed
slope parameter for effect of individual body size
mean value of population random intercept term
standard deviation of population random effect
Binomial sample size
Of the three scenarios considered, Binomial sample size had the smallest effect on parameter accuracy and precision. As with the Random Effects scenario, increasing Binomial sample size did not remedy the bias in βprey caused by overdispersion, but did yield slightly higher precision (yellow circles, Fig. 3). Estimates for all other parameters were similar irrespective of Binomial sample size and type of overdispersion (blue circles, yellow diamonds and blue diamonds, Fig. 3), and the proportion of models where βbodysize > 0 were fairly constant across all values tested (Fig. 4C). Summary: OLRE models on Beta-Binomial data continue to perform poorly in the presence of overdispersion, irrespective of the Binomial sample size. All other models performed equally well, and there was evidence suggesting that even over the narrow range of sample sizes tested (2–10), precision increased with sample size.
A comparison of frequentist and Bayesian beta-binomial estimates
Model estimates from the Beta-Binomial models were very similar irrespective of whether they were from frequentist or Bayesian models (Fig. 1 and Table 2B). Interestingly, both the frequentist and Bayesian models suggested that using Beta-Binomial models on overdispersed Binomial data leads to underestimating the values of both of μpop (middle pane, Fig. 1; Table 2C) and βprey (left pane, Fig. 1; Table 2B). Collectively these results suggest the discrepancies in parameter estimates observed between OLRE and Beta-Binomial models were not simply due to using either Bayesian or frequentist methods, but reflected a genuine difference in ability of certain models to handle certain types of overdispersion.
Using a simulation approach, I have investigated both the ability of observation-level random effects to recover accurate parameter estimates under various degrees of overdispersion in Binomial models, and whether the performance of OLRE in mixed models is consistent across multiple types of overdispersion. In addition, I have examined how model performance changes in the presence of overdispersion when both the sample size of random effects (number of levels) and Binomial sample size is low for both OLRE and Beta-Binomial models. In general, OLRE models performed poorly when fitted to Beta-Binomial data, and this effect was particularly pronounced when the number levels of the random effect was ≤5, or the Binomial sample size was small. In all cases, increasing the number of levels of the random effect or Binomial sample size failed to remedy the bias in the estimates caused by overdispersion. Here I discuss the implications of these results for choosing OLRE as a suitable tool to model overdispersion in ecological data.
The ability of OLRE to cope with overdispersion depends on the process generating the overdispersion
This study has shown that the ability of OLRE to recover accurate parameter estimates in overdispersed mixed models depends on the process generating the overdispersion in the dataset. For overdispersion generated by adding random noise to the linear predictor (overdispersed Binomial data), the model recovered accurate mean estimates for slopes, intercepts and variance components at all levels of overdispersion, although the precision of the estimates declined (increasing standard errors) as overdispersion increased. Conversely, for data generated using a Beta-Binomial process, parameter estimates became increasingly more biased as overdispersion increased, leading to inflated estimates of effect size for the variables of interest. The implications of these results are that OLRE may not be a robust tool for dealing with overdispersion in Binomial mixed models because the researcher is unlikely to know which process generated the overdispersion in the first instance, meaning it is unclear if the parameter estimates are trustworthy. More worryingly, that the use of OLRE can lead to inflated effect sizes may result in researchers concluding that variables under investigation are highly influential, when in fact their effect sizes may be more modest. These patterns were not observed in the Beta-Binomial models, irrespective of data type, although the confidence intervals did also increase in concert with overdispersion. The relative utility of Beta-Binomial models over OLRE models is discussed below.
Increasing random effect sample size increases precision, even for biased estimates
For all model/data combinations, increasing the number of populations measured from 3 to 20 greatly increased the precision of estimates for all parameters, reflected by smaller 95% confidence intervals. Importantly, this result held even when controlling for total sample size, demonstrating that higher replication within populations cannot compensate for fitting a population random intercept term with only 3 levels. Arguably, parameter precision was also poor when using 5 populations, especially for the variance component σpop. This corroborates the general rule of thumb that random intercept terms should ideally contain more than 5 levels in order to yield accurate estimates and good model performance (Gelman & Hill, 2006), especially when overdispersion is present. The key result of the random effects simulations is that when using OLRE on Beta-Binomial data, increasing sample size of the random effect does nothing to remedy the bias in slope estimates for effects such as βprey. Instead, increasing the number of levels of the random effect simply makes one more certain of the accuracy of the estimates by decreasing the 95% confidence intervals, even when the mean estimate is biased. This is worrying, as well-replicated studies studying 10s to 100s of ‘groups’ (be they populations, genetic lines or sampling locations etc.) may recover highly precise estimates for parameters that are highly inflated with respect to their true value. In addition, it means one cannot use enormous standard errors as diagnostic evidence for suspicious behavior of OLRE (i.e., for βprey when n populations =3) because these are likely to change with sample size.
Parameter estimates are largely similar irrespective of Binomial sample size
Binomial sample size had the smallest influence on model behavior of the three scenarios tested. There was some indication that parameter accuracy increased with Binomial sample size, but these effects were modest for most data/model combinations, especially when compared to the relatively large influence of random effect sample size (see above). Such a result is intuitive, as one would expect total sample size to be more influential than simply the sample size of a single observational unit. That model precision is similar irrespective of maximum sample size is encouraging, particularly as researchers in the fields of ecology and evolution deal with an enormous range in sample sizes e.g., analyzing the hatching success of a bird that produces only 2 eggs at a time (Harrison et al., 2013b) or an insect that produces 100s of eggs at a time (Tyler et al., 2013). Collectively these results suggest that model precision need not be sacrificed if working on organisms with life history characteristics such as small clutch size or low fecundity.
Are Beta-Binomial models more robust than OLRE models for Binomial data?
A persistent pattern in the results shown here is that OLRE perform poorly for Beta-Binomial data, yet Beta-Binomial models tend to perform well across both Beta-Binomial and overdispersed Binomial data. This does not mean that OLRE models are unsuitable for modeling overdispersion, simply that one must interpret initial model results with caution and examine the suitability of OLRE for a particular dataset . The most straightforward way to probe the robustness of OLRE model results would be to compare parameter estimates from a given OLRE model with the Beta-Binomial equivalent. There were dramatic differences in estimates for the βprey slope between models types for the Beta-Binomial data, yet very little difference for the corresponding overdispersed Binomial data, and so the comparative approach should readily identify potential problems with OLRE.
When performing model comparison, whether one uses a frequentist statistical package, or codes the model manually in a Bayesian framework in JAGS appears to be a matter of preference, as these two approaches yielded similar results in the current study. Although Bayesian methods do have several advantages over frequentist methods (e.g., Ellison, 2004; Kery, 2010), in many cases they recover similar parameter estimates to frequentist models when uninformative priors are used (e.g., Kery, 2010). In support of this, sensitivity analyses presented in this paper suggest that model results are similar whether one uses a Bayesian or a frequentist Beta-Binomial model, meaning choice of model rather than of statistical philosophy is the more important driver here. However, I would caution that I only repeated the frequentist Beta-Binomial simulations for a limited subset of cases (high overdispersion, 10 populations, 20 individuals per population), and so it should not be assumed that frequentist and Bayesian approaches would agree in every case (see also examples in Kery, 2010). A final caveat for these results is that the data generated in this study are ‘ideal’ in so far as they are perfectly balanced across populations (identical numbers of individuals per population). In reality this is unlikely to hold, as ecological datasets often contain poorly represented groups with far lower sample sizes than others e.g., years with poor breeding success and limited data on clutch size (e.g., Harrison et al., 2011; Harrison et al., 2013b). How imbalance in sample size affects model estimates in the presence of overdispersion for two models with identical number of levels of the random intercept term warrants further investigation.
Superficially, it appears that Beta-Binomial models perform better than OLRE models in most cases, and so a natural inclination would be to simply use Beta-Binomial models for any kind of overdispersed Binomial data. However, results from this study indicated that Beta-Binomial models can underestimate slope values (e.g., the value of βprey), whereas the corresponding OLRE model does not. Thus, Beta-Binomial models do not universally outperform OLRE models, and one should not sacrifice OLRE from the set of tools available to deal with overdispersed Binomial data. Both Beta-Binomial and OLRE models estimate an overdispersion parameter that can reveal a biological cause underlying aggregation/non-independence of probabilities in the dataset (e.g., Beta-Binomial models, Hilgenboecker et al., 2008; OLRE models, Elston et al., 2001, and the size of the aggregation parameter is informative and comparable across studies (Richards, 2008)). Beta-binomial models are frequently employed in the ecological literature to model non-independence among probabilities (e.g., Hughes & Madden, 1993; Lee & Nelder, 1996; Clark, 2003; Richards, 2008) and may be less prone to overfitting than the corresponding OLRE models, which may explain why OLRE models performed poorly for Beta-Binomial data. Indeed, the dispersion parameter of models containing OLRE frequently collapses to <0.5 (data not shown), suggesting the addition of OLRE replaces overdispersion with underdispersion, which can be equally as problematic (Zuur et al., 2009).
Observation-level random effects provide a simple means to control overdispersion that can be easily implemented in mixed effects model packages. However, it is clear that their use may not be appropriate in all cases. Results from models containing OLRE should be carefully inspected, and where possible corroborative evidence should be sought from alternative modeling approaches such as (Bayesian) Hierarchical Beta-Binomial models to quantify agreement between parameter estimates and ensure the conclusions drawn from such analyses are robust. Finally, one should avoid fitting random intercept terms to models when the random term contain <5 levels, especially in the presence of overdispersion, as parameter estimates become unreliable irrespective of modeling approach. One should also interpret model results with caution when the random effect sample size is large (e.g., >20), because models with OLRE can yield inaccurate but precise (small confidence intervals) slope estimates under certain scenarios that may give the false impression of that model having performed well.