Examples

library(rPBK)

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 0.000122 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:  Elapsed Time: 1.481 seconds (Warm-up)
#> Chain 1:                1.299 seconds (Sampling)
#> Chain 1:                2.78 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.0001 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                3.32 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.00011 seconds
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#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 2.308 seconds (Warm-up)
#> Chain 3:                0.696 seconds (Sampling)
#> Chain 3:                3.004 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'PBK_AD' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 8.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.87 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 1.585 seconds (Warm-up)
#> Chain 4:                1.119 seconds (Sampling)
#> Chain 4:                2.704 seconds (Total)
#> Chain 4:
#> Warning: There were 2403 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
plot(fitPBK_MGS)

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 0.00137 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 13.7 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 117.057 seconds (Warm-up)
#> Chain 1:                47.546 seconds (Sampling)
#> Chain 1:                164.603 seconds (Total)
#> Chain 1:
#> Warning: There were 480 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
plot(fitPBK_C4)

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: 
#> 17 (20.2%) 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   -231.4 17.1
#> p_waic        18.4  2.4
#> waic         462.8 34.1
#> 
#> 17 (20.2%) 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)
#> Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
print(LOO_C4)
#> 
#> Computed from 500 by 84 log-likelihood matrix
#> 
#>          Estimate   SE
#> elpd_loo   -231.0 17.0
#> p_loo        18.0  2.3
#> looic       462.0 34.0
#> ------
#> Monte Carlo SE of elpd_loo is 1.7.
#> 
#> Pareto k diagnostic values:
#>                          Count Pct.    Min. n_eff
#> (-Inf, 0.5]   (good)     83    98.8%   2         
#>  (0.5, 0.7]   (ok)        1     1.2%   3         
#>    (0.7, 1]   (bad)       0     0.0%   <NA>      
#>    (1, Inf)   (very bad)  0     0.0%   <NA>      
#> 
#> All Pareto k estimates are ok (k < 0.7).
#> 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 0.001871 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 18.71 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 9.832 seconds (Warm-up)
#> Chain 1:                23.241 seconds (Sampling)
#> Chain 1:                33.073 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: 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 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
plot(fitPBK_C42)

log_lik_C42 <- loo::extract_log_lik(fitPBK_C42$stanfit, merge_chains = FALSE)
WAIC_C42 <- waic(log_lik_C42)
#> Warning: 
#> 10 (11.9%) 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   -315.7 11.3
#> p_waic        14.0  2.0
#> waic         631.5 22.6
#> 
#> 10 (11.9%) 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 -84.3       7.7

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).