Perform model selection using leave one out cross validation of model objects

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 
# }

Perform model selection using leave one out cross validation of model objects

Usage

Arguments

Value

Note

Examples

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