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

Source: R/joint_select.R
joint_select.Rd

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 joint_model() or models fits using traditional_model(). See more examples in the Package Vignette.

Usage

joint_select(model_fits)

Arguments

model_fits

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 joint_model() or all with traditional_model().

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

Examples

# \donttest{
data(green_crab_data)

# 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 <- joint_model(data = green_crab_data, family = "poisson",
                        p10_priors = c(1,20), q = FALSE, multicore = FALSE)
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.7 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: 0.886 seconds (Warm-up)
#> Chain 1:                1.748 seconds (Sampling)
#> Chain 1:                2.634 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 6.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.63 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: 0.857 seconds (Warm-up)
#> Chain 2:                2.082 seconds (Sampling)
#> Chain 2:                2.939 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 6.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.67 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: 0.873 seconds (Warm-up)
#> Chain 3:                1.358 seconds (Sampling)
#> Chain 3:                2.231 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 6.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.68 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: 0.865 seconds (Warm-up)
#> Chain 4:                2.233 seconds (Sampling)
#> Chain 4:                3.098 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 <- joint_model(data = green_crab_data, family = "negbin",
                     p10_priors = c(1,20), q = TRUE, multicore = FALSE)
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000315 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.15 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: 4.08 seconds (Warm-up)
#> Chain 1:                12.41 seconds (Sampling)
#> Chain 1:                16.49 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000348 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.48 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: 3.509 seconds (Warm-up)
#> Chain 2:                11.182 seconds (Sampling)
#> Chain 2:                14.691 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000311 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.11 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: 4.045 seconds (Warm-up)
#> Chain 3:                11.587 seconds (Sampling)
#> Chain 3:                15.632 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000315 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.15 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: 3.934 seconds (Warm-up)
#> Chain 4:                10.881 seconds (Sampling)
#> Chain 4:                14.815 seconds (Total)
#> Chain 4: 
#> Refer to the eDNAjoint guide for visualization tips:  https://ednajoint.netlify.app/tips#visualization-tips 

# Perform model selection
joint_select(model_fits = list(fit_no_q$model, fit_q$model))
#>        elpd_diff se_diff
#> model2    0.0       0.0 
#> model1 -164.8      39.0 
# }