Installation

install.packages("BeeGUTS")

# install.packages("devtools")devtools::install_github("bgoussen/BeeGUTS")

Documentation

Example

library(BeeGUTS)#> BeeGUTS (Version 1.3.0, packaged on the: )#> - For execution on a local, multicore CPU with excess RAM we recommend calling#>       options(mc.cores = parallel::detectCores()-1)#> - In addition to the functions provided by 'BeeGUTS', we recommend using the packages:#>    - 'bayesplot' for posterior analysis, model checking, and MCMC diagnostics.#>    - 'loo' for leave-one-out cross-validation (LOO) using Pareto smoothed#>        importance sampling (PSIS), comparison of predictive errors between models, and#>        widely applicable information criterion (WAIC).file_location<- system.file("extdata","betacyfluthrin_chronic_ug.txt",package="BeeGUTS")# Load the path to one of the example filelsData<- dataGUTS(file_location=file_location,test_type='Chronic_Oral',cstConcCal=FALSE)# Read the example fileplot(lsData)# Plot the data#> [[1]]

fit<- fitBeeGUTS(lsData,modelType="SD",nIter=3000,nChains=1)# Fit a SD model. This can take some time...#>#> SAMPLING FOR MODEL 'GUTS_SD' NOW (CHAIN 1).#> Chain 1:#> Chain 1: Gradient evaluation took 0.001194 seconds#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 11.94 seconds.#> Chain 1: Adjust your expectations accordingly!#> Chain 1:#> Chain 1:#> Chain 1: Iteration:    1 / 3000 [  0%]  (Warmup)#> Chain 1: Iteration:  300 / 3000 [ 10%]  (Warmup)#> Chain 1: Iteration:  600 / 3000 [ 20%]  (Warmup)#> Chain 1: Iteration:  900 / 3000 [ 30%]  (Warmup)#> Chain 1: Iteration: 1200 / 3000 [ 40%]  (Warmup)#> Chain 1: Iteration: 1500 / 3000 [ 50%]  (Warmup)#> Chain 1: Iteration: 1501 / 3000 [ 50%]  (Sampling)#> Chain 1: Iteration: 1800 / 3000 [ 60%]  (Sampling)#> Chain 1: Iteration: 2100 / 3000 [ 70%]  (Sampling)#> Chain 1: Iteration: 2400 / 3000 [ 80%]  (Sampling)#> Chain 1: Iteration: 2700 / 3000 [ 90%]  (Sampling)#> Chain 1: Iteration: 3000 / 3000 [100%]  (Sampling)#> Chain 1:#> Chain 1:  Elapsed Time: 165.434 seconds (Warm-up)#> Chain 1:                81.417 seconds (Sampling)#> Chain 1:                246.851 seconds (Total)#> Chain 1:#> 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-esstraceplot(fit)# Produce a diagnostic plot of the fit

plot(fit)# Plot the fit results#> [[1]]

summary(fit)# Gives a summary of the results#> Computing summary can take some time. Please be patient...Summary:#>#> Bayesian Inference performed with Stan.#>  Model type: SD#>  Bee species: Honey_Bee#>#>  MCMC sampling setup (select with '$setupMCMC')#>  Iterations: 3000#>  Warmup iterations: 1500#>  Thinning interval: 1#>  Number of chains: 1#>#> Priors of the parameters (quantiles) (select with '$Qpriors'):#>#>  parameters      median        Q2.5       Q97.5#>          hb 8.32763e-03 1.09309e-04 6.34432e-01#>          kd 2.62826e-03 1.17073e-06 5.90041e+00#>          zw 8.24621e-03 1.19783e-06 5.67693e+01#>          bw 1.84061e-03 1.69711e-06 1.99625e+00#>#> Posteriors of the parameters (quantiles) (select with '$Qposteriors'):#>#>  parameters      median        Q2.5       Q97.5#>       hb[1] 6.90363e-03 2.36523e-03 9.96659e-03#>  parameters      median        Q2.5       Q97.5#>          kd 1.00619e+00 7.33489e-01 2.48421e+00#>          zw 9.46929e+00 5.59388e+00 1.07653e+01#>          bw 8.86478e-03 6.19304e-03 1.05303e-02#>#>#>  Maximum Rhat computed (na.rm = TRUE): 1.070234#>  Minimum Bulk_ESS: 35#>  Minimum Tail_ESS: 19#>  Bulk_ESS and Tail_ESS are crude measures of effecting sampling size for#>       bulk and tail quantities respectively. An ESS > 100 per chain can be#>       considered as a good indicator. Rhat is an indicator of chains convergence.#>       A Rhat <= 1.05 is a good indicator of convergence. For detail results,#>       one can call 'rstan::monitor(YOUR_beeSurvFit_OBJECT$stanFit)#>#>  EFSA Criteria (PPC, NRMSE, and SPPE) can be accessed via 'x$EFSA'validation<- validate(fit,lsData,fithb=TRUE)# produce a validation of the fit after refitting background mortality from the control data (here it uses the same dataset as calibration as an example, so not relevant…)#> Fitting the background mortality parameter on the control data of the#>         validation dataset.#> Warning in lsOUT$nDatasets <- nDatasets: Wandle linke Seite in eine Liste um#>#> SAMPLING FOR MODEL 'GUTS_hb_only' NOW (CHAIN 1).#> Chain 1:#> Chain 1: Gradient evaluation took 1.9e-05 seconds#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.#> Chain 1: Adjust your expectations accordingly!#> Chain 1:#> Chain 1:#> Chain 1: Iteration:    1 / 3000 [  0%]  (Warmup)#> Chain 1: Iteration:  300 / 3000 [ 10%]  (Warmup)#> Chain 1: Iteration:  600 / 3000 [ 20%]  (Warmup)#> Chain 1: Iteration:  900 / 3000 [ 30%]  (Warmup)#> Chain 1: Iteration: 1200 / 3000 [ 40%]  (Warmup)#> Chain 1: Iteration: 1500 / 3000 [ 50%]  (Warmup)#> Chain 1: Iteration: 1501 / 3000 [ 50%]  (Sampling)#> Chain 1: Iteration: 1800 / 3000 [ 60%]  (Sampling)#> Chain 1: Iteration: 2100 / 3000 [ 70%]  (Sampling)#> Chain 1: Iteration: 2400 / 3000 [ 80%]  (Sampling)#> Chain 1: Iteration: 2700 / 3000 [ 90%]  (Sampling)#> Chain 1: Iteration: 3000 / 3000 [100%]  (Sampling)#> Chain 1:#> Chain 1:  Elapsed Time: 0.117 seconds (Warm-up)#> Chain 1:                0.109 seconds (Sampling)#> Chain 1:                0.226 seconds (Total)#> Chain 1:#> Bayesian Inference performed with Stan.#>  MCMC sampling setup (select with '$setupMCMC')#>  Iterations: 3000#>  Warmup iterations: 1500#>  Thinning interval: 1#>  Number of chains: 1#>#> Maximum Rhat computed (na.rm = TRUE): 1.003735#>  Minimum Bulk_ESS: 434#>  Minimum Tail_ESS: 384#>  Bulk_ESS and Tail_ESS are crude measures of effecting sampling size for#>       bulk and tail quantities respectively. An ESS > 100 per chain can be#>       considered as a good indicator. Rhat is an indicator of chains convergence.#>       A Rhat <= 1.05 is a good indicator of convergence. For detail results,#>       one can call 'rstan::monitor(beeSurvValidation$hbfit)#>#> Results for hb:#>  parameters      median         Q2.5       Q97.5#>          hb 0.002590227 0.0006998442 0.006610267#> Note that computing can be quite long (several minutes).#>   Tips: To reduce that time you can reduce Number of MCMC chains (default mcmc_size is set to 1000).plot(validation)# plot the validation results

dataPredict<-data.frame(time= c(1:5,1:15),conc= c(rep(5,5), rep(15,15)),replicate= c(rep("rep1",5), rep("rep3",15)))# Prepare data for forwards predictionprediction<- predict(fit,dataPredict)# Perform forwards prediction. At the moment, no concentration recalculation is performed in the forwards prediction. The concentrations are taken as in a chronic testplot(prediction)# Plot of the prediction results