Report quantiles of gofstats values for observed data relative to posterior predictive distribution

Usage

gofstats(mod, thin = T, thin_to = 300)

Arguments

mod: An object of class "compnet" created by the buildcompnet() function.
thin: Logical value indicating whether to take a random subsample of posterior draws.
thin_to: Logical value indicating the size of the subsample to take if thin=TRUE.

Value

A named vector containing quantiles on the interval \([0,1]\) for: 1- the standard deviation of row means, and 2- The triadic dependency metric used by Hoff, Fosdick, & Volfovsky's "amen" package. These values represent the proportion of the posterior predictive simulation that were less than the value for the observed data.

Details

This function can be used to assess whether species-level and higher-order dependencies in the data are represented adequately by the model structure. Extreme output values indicate there may be a problem. In these cases, it may help to use different fixed effect predictors, or to increase the model rank.

Examples


data(ex_presabs)
data(ex_traits)

# Quick demo run. Will prompt warnings.
# Run with default warmup and iter for good posterior sampling.
ex_compnet <- buildcompnet(presabs=ex_presabs, spvars_dist_int=ex_traits[c("ndtrait")], warmup=10, iter=20)
#> 
#> SAMPLING FOR MODEL 'srm_binomial' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.79 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: No variance estimation is
#> Chain 1:          performed for num_warmup < 20
#> Chain 1: 
#> Chain 1: Iteration:  1 / 20 [  5%]  (Warmup)
#> Chain 1: Iteration:  2 / 20 [ 10%]  (Warmup)
#> Chain 1: Iteration:  4 / 20 [ 20%]  (Warmup)
#> Chain 1: Iteration:  6 / 20 [ 30%]  (Warmup)
#> Chain 1: Iteration:  8 / 20 [ 40%]  (Warmup)
#> Chain 1: Iteration: 10 / 20 [ 50%]  (Warmup)
#> Chain 1: Iteration: 11 / 20 [ 55%]  (Sampling)
#> Chain 1: Iteration: 12 / 20 [ 60%]  (Sampling)
#> Chain 1: Iteration: 14 / 20 [ 70%]  (Sampling)
#> Chain 1: Iteration: 16 / 20 [ 80%]  (Sampling)
#> Chain 1: Iteration: 18 / 20 [ 90%]  (Sampling)
#> Chain 1: Iteration: 20 / 20 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.004 seconds (Warm-up)
#> Chain 1:                0.022 seconds (Sampling)
#> Chain 1:                0.026 seconds (Total)
#> Chain 1: 
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.12, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> [1] "compnet uses Stan under the hood. You may see warnings from Stan alongside, this message. To deal with any warnings Stan might issue, Please see the links provided in Stan's output, as well as the compnet website:https://kyle-rosenblad.github.io/compnet/"

gofstats(ex_compnet)
#> Approx. completion
#> 50%
#> 100%
#> p.sd.rowmeans   p.cycle.dep 
#>           0.1           0.9