(ref geefigureref)

(glmm)

practiced

ifelse(DEPLY==2, TRUE, FALSE)

Here, ΣΣ\Sigma is the variance/covariance matrix for all of the observations. This matrix is what is referred to as block diagonal. The 0’s represent covariances for observations from different sites – these are 0, since we assume observations from different sites are independent. The ViViV_i contain the variance/covariances for cluster iii (eq. (18.10)).

the

FIGURE 3.8: BLUPs for b0ib0ib_{0i} and b1ib1ib_{1i}, along with their uncertainty, plotted using the plot_model function in the sjPlot package.

that we have considered is that

model think

= be

modeling modeling

There are other options for comparing models fit in a Bayesian framework. Two popular options are LOO (Vehtari & Lampinen, 2002) and WAIC (Watanabe & Opper, 2010), which are available in the loo package (Vehtari et al., 2020). Again, I recommend reading Hooten & Hobbs (2015) to learn more about these metrics; see also Vehtari, Gelman, & Gabry (2017).

## beta[2] 0.000 0.000 0.000 0.000 0.000 1 18777

logL(^θs|y)logL(θ^s|y)logL(\hat{\theta}_s | y)

The goodness-of-fit tests suggest there are problems with this model

Let’s also load some other packages that will be useful in this section:

FIGURE 11.4: Posterior distribution for p, the probability of detection a moose from a helicopter in MN surveys.

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