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This function performs leave one out cross validation of a list of model fits using functions in the loo package, as described in Vehtari, Gelman, and Gabry (2017) doi:10.1007/s11222-016-9696-4. Compare models fit using jointModel() or models fits using traditionalModel(). See more examples in the Package Vignette.

Usage

jointSelect(modelfits)

Arguments

modelfits

A list containing model fits of class stanfit.

Value

A matrix of delta elpd (expected log pointwise predictive density) between model fits. Function is performed using the loo package.

Note

Before model selection, this function makes the following check:

  • Input is a list of model fits of class 'stanfit'.

  • All models compared were fit wither either jointModel() or all with traditionalModel().

If any of these checks fail, the function returns an error message.

Examples

# \donttest{
data(greencrabData)

# Fit a model without estimating a gear scaling coefficient for traditional
# survey gear types.
# This model assumes all traditional survey methods have the same
# catchability.
# Count data is modeled using a poisson distribution.
fit.no.q <- jointModel(data = greencrabData, family = "poisson",
                       p10priors = c(1,20), q = FALSE, multicore = FALSE)
#> 
#> SAMPLING FOR MODEL 'joint_binary_pois' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 1: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 1: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 1: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 1: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 1: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 1: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.922 seconds (Warm-up)
#> Chain 1:                2.175 seconds (Sampling)
#> Chain 1:                3.097 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_pois' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 6.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 2: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 2: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 2: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 2: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 2: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 2: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.864 seconds (Warm-up)
#> Chain 2:                2.092 seconds (Sampling)
#> Chain 2:                2.956 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_pois' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 6.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 3: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 3: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 3: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 3: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 3: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 3: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.946 seconds (Warm-up)
#> Chain 3:                1.494 seconds (Sampling)
#> Chain 3:                2.44 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_pois' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 6.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 4: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 4: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 4: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 4: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 4: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 4: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.894 seconds (Warm-up)
#> Chain 4:                2.082 seconds (Sampling)
#> Chain 4:                2.976 seconds (Total)
#> Chain 4: 
#> Refer to the eDNAjoint guide for visualization tips:  https://ednajoint.netlify.app/tips#visualization-tips 


# Fit a model estimating a gear scaling coefficient for traditional
# survey gear types.
# This model does not assume all traditional survey methods have the
# same catchability.
# Gear type 1 is used as the reference gear type.
# Count data is modeled using a negative binomial distribution.
fit.q <- jointModel(data = greencrabData, family = "negbin",
                    p10priors = c(1,20), q = TRUE, multicore = FALSE)
#> 
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000297 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.97 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 1: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 1: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 1: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 1: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 1: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 1: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 3.191 seconds (Warm-up)
#> Chain 1:                8.04 seconds (Sampling)
#> Chain 1:                11.231 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000338 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.38 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 2: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 2: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 2: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 2: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 2: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 2: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 3.358 seconds (Warm-up)
#> Chain 2:                10.617 seconds (Sampling)
#> Chain 2:                13.975 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000285 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 2.85 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 3: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 3: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 3: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 3: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 3: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 3: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 3.457 seconds (Warm-up)
#> Chain 3:                8.687 seconds (Sampling)
#> Chain 3:                12.144 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_binary_catchability_negbin' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000306 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.06 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 3000 [  0%]  (Warmup)
#> Chain 4: Iteration:  500 / 3000 [ 16%]  (Warmup)
#> Chain 4: Iteration:  501 / 3000 [ 16%]  (Sampling)
#> Chain 4: Iteration: 1000 / 3000 [ 33%]  (Sampling)
#> Chain 4: Iteration: 1500 / 3000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 2000 / 3000 [ 66%]  (Sampling)
#> Chain 4: Iteration: 2500 / 3000 [ 83%]  (Sampling)
#> Chain 4: Iteration: 3000 / 3000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 3.533 seconds (Warm-up)
#> Chain 4:                9.138 seconds (Sampling)
#> Chain 4:                12.671 seconds (Total)
#> Chain 4: 
#> Refer to the eDNAjoint guide for visualization tips:  https://ednajoint.netlify.app/tips#visualization-tips 

# Perform model selection
jointSelect(modelfits = list(fit.no.q$model, fit.q$model))
#>        elpd_diff se_diff
#> model2    0.0       0.0 
#> model1 -164.8      39.0 
# }