Single Compartment and Single Exposure : Male Gammarus Single
data("dataMaleGammarusSingle")
# work only when replicate have the same length !!!
data_MGS <- dataMaleGammarusSingle[dataMaleGammarusSingle$replicate == 1,]
modelData_MGS <- dataPBK(
object = data_MGS,
col_time = "time",
col_replicate = "replicate",
col_exposure = "expw",
col_compartment = "conc",
time_accumulation = 4,
nested_model = NA)
fitPBK_MGS <- fitPBK(modelData_MGS)
#>
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 5.2e-05 seconds
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#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 3).
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#> Chain 4:
#> Warning: There were 2227 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
library(loo)
#> This is loo version 2.6.0
#> - Online documentation and vignettes at mc-stan.org/loo
#> - As of v2.0.0 loo defaults to 1 core but we recommend using as many as possible. Use the 'cores' argument or set options(mc.cores = NUM_CORES) for an entire session.
log_lik_MGS <- loo::extract_log_lik(fitPBK_MGS$stanfit, merge_chains = FALSE)
WAIC_MGS <- waic(log_lik_MGS)
#> Warning:
#> 1 (12.5%) p_waic estimates greater than 0.4. We recommend trying loo instead.
Multiple Compartiment, Single Exposure - Default interaction
data("dataCompartment4")
data_C4 <- dataCompartment4
modelData_C4 <- dataPBK(
object = data_C4,
col_time = "temps",
col_replicate = "replicat",
col_exposure = "condition",
col_compartment = c("intestin", "reste", "caecum", "cephalon"),
time_accumulation = 7)
You can have a look at the assumption on the interaction
nested_model(modelData_C4)
#> $ku_nest
#> uptake intestin uptake reste uptake caecum uptake cephalon
#> 1 1 1 1
#>
#> $ke_nest
#> excretion intestin excretion reste excretion caecum excretion cephalon
#> 1 1 1 1
#>
#> $k_nest
#> intestin reste caecum cephalon
#> intestin 0 1 1 1
#> reste 1 0 1 1
#> caecum 1 1 0 1
#> cephalon 1 1 1 0
fitPBK_C4 <- fitPBK(modelData_C4, chains = 1, iter = 1000)
#>
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000897 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 8.97 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: Elapsed Time: 20.905 seconds (Warm-up)
#> Chain 1: 179.312 seconds (Sampling)
#> Chain 1: 200.217 seconds (Total)
#> Chain 1:
#> Warning: There were 432 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 68 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
#> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
Compute WAIC with loo library:
library(loo)
log_lik_C4 <- loo::extract_log_lik(fitPBK_C4$stanfit, merge_chains = FALSE)
WAIC_C4 <- waic(log_lik_C4)
#> Warning:
#> 6 (7.1%) p_waic estimates greater than 0.4. We recommend trying loo instead.
print(WAIC_C4)
#>
#> Computed from 500 by 84 log-likelihood matrix
#>
#> Estimate SE
#> elpd_waic -255.5 18.0
#> p_waic 10.6 1.7
#> waic 511.0 36.0
#>
#> 6 (7.1%) p_waic estimates greater than 0.4. We recommend trying loo instead.
Compute LOO:
r_eff_C4 <- relative_eff(exp(log_lik_C4))
LOO_C4 <- loo(log_lik_C4, r_eff = r_eff_C4, cores = 2)
print(LOO_C4)
#>
#> Computed from 500 by 84 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -255.6 18.0
#> p_loo 10.6 1.7
#> looic 511.2 36.0
#> ------
#> Monte Carlo SE of elpd_loo is 0.9.
#>
#> All Pareto k estimates are good (k < 0.5).
#> See help('pareto-k-diagnostic') for details.
Multiple Compartiment, Single Exposure : Change nesting
You can have a look at the assumption on the interaction
nm_C4 = nested_model(modelData_C4)
We want to change the interaction between organs. For now, all organs
interact with each other but not with themselve, the the interaction
matrix look like:
nm_C4$k_nest
#> intestin reste caecum cephalon
#> intestin 0 1 1 1
#> reste 1 0 1 1
#> caecum 1 1 0 1
#> cephalon 1 1 1 0
which can be written like:
matrix(c(
c(0,1,1,1),
c(1,0,1,1),
c(1,1,0,0),
c(1,1,1,0)),
ncol=4,nrow=4,byrow=TRUE)
#> [,1] [,2] [,3] [,4]
#> [1,] 0 1 1 1
#> [2,] 1 0 1 1
#> [3,] 1 1 0 0
#> [4,] 1 1 1 0
Let assume interaction are only one way, so a triangular matrix:
matrix(c(
c(0,1,1,1),
c(0,0,1,1),
c(0,0,0,0),
c(0,0,0,0)),
ncol=4,nrow=4,byrow=TRUE)
#> [,1] [,2] [,3] [,4]
#> [1,] 0 1 1 1
#> [2,] 0 0 1 1
#> [3,] 0 0 0 0
#> [4,] 0 0 0 0
modelData_C42 <- dataPBK(
object = data_C4,
col_time = "temps",
col_replicate = "replicat",
col_exposure = "condition",
col_compartment = c("intestin", "reste", "caecum", "cephalon"),
time_accumulation = 7,
ku_nest = c(1,1,1,1), # No Change here
ke_nest = c(1,1,1,1), # No Change here
k_nest = matrix(c(
c(0,1,1,1),
c(0,0,1,1),
c(0,0,0,0),
c(0,0,0,0)),
ncol=4,nrow=4,byrow=TRUE) # Remove
)
nested_model(modelData_C42)
#> $ku_nest
#> uptake intestin uptake reste uptake caecum uptake cephalon
#> 1 1 1 1
#>
#> $ke_nest
#> excretion intestin excretion reste excretion caecum excretion cephalon
#> 1 1 1 1
#>
#> $k_nest
#> intestin reste caecum cephalon
#> intestin 0 1 1 1
#> reste 0 0 1 1
#> caecum 0 0 0 0
#> cephalon 0 0 0 0
fitPBK_C42 <- fitPBK(modelData_C42, chains = 1, iter = 1000)
#>
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000756 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.56 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#> Chain 1: Elapsed Time: 9.878 seconds (Warm-up)
#> Chain 1: 10.872 seconds (Sampling)
#> Chain 1: 20.75 seconds (Total)
#> Chain 1:
#> Warning: There were 500 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
log_lik_C42 <- loo::extract_log_lik(fitPBK_C42$stanfit, merge_chains = FALSE)
WAIC_C42 <- waic(log_lik_C42)
#> Warning:
#> 3 (3.6%) p_waic estimates greater than 0.4. We recommend trying loo instead.
print(WAIC_C42)
#>
#> Computed from 500 by 84 log-likelihood matrix
#>
#> Estimate SE
#> elpd_waic -279.8 14.3
#> p_waic 6.5 1.4
#> waic 559.7 28.6
#>
#> 3 (3.6%) p_waic estimates greater than 0.4. We recommend trying loo instead.
Compare WAIC with previous model
comp_C4_C42 <- loo_compare(WAIC_C4, WAIC_C42)
print(comp_C4_C42)
#> elpd_diff se_diff
#> model1 0.0 0.0
#> model2 -24.3 10.2
The first column shows the difference in ELPD relative to the model
with the largest ELPD. In this case, the difference in elpd and its
scale relative to the approximate standard error of the difference)
indicates a preference for the second model (model2).