Please report any issue | Loading morse and its dependencies (JAGS, C++) | JAGS | C++ | Survival analysis | Fit | ... with plot | LCx | MFX | Predict survival probability | Predict number of survival

Survival toxicity tests | Analysis of target time survival toxicity tests | Modelling | Inference | Toxicokinetic-Toxicodynamic modeling | GUTS Modelling | GUTS-SD | GUTS-IT | For constant concentration exposure | Reproduction toxicity tests | Estimation of the effective observation period | Target time analysis | References

Survival data analysis at target time (TT) | Step 1: check the structure and the data set integrity | Step 2: create a survData object | Step 3: visualize your data set | Step 4: fit an exposure-response model to the survival data at target time | Step 5: validate the model with a posterior predictive check | Survival analysis using a toxicokinetic-toxicodynamic (TKTD) model: GUTS | GUTS model with constant exposure concentrations | GUTS model "SD" | Fix background mortality | GUTS model "IT" | GUTS model under time-variable exposure concentration | Computing prediction | Robust ODE solver with deSolve | Removing background mortality in predictions | Validation criteria: EFSA recommendations | Computing predictions with number of survivors | Robust implementation with ODE solver predict_Nsurv_ode | Validation criteria | Plot and PPC of predict_Nsurv objects | Additional options for EFSA user | Lethal concentration | Multiplication factors: 'margin of safety' | Change the level of multiplication factor | Change the default time of multiplication factor assessment | Compute a range of multiplication factors | Multiplication factor when hb is fixed | Reproduction data analysis at target-time | Model comparison | Reproduction data and survival functions

1. Landscape and Habitats | 2. The Trophic Web | 3. The Spacemodel: The Core Object | 4. Dispersal and Movement | 5. Exposure and Contaminant Transfer | 6. Ecological Risk Mapping | Next Steps

1. Le Paysage et les Habitats | 2. Le Réseau Trophique | 3. Le Spacemodel : l'objet central | 4. La Dispersion et le Mouvement | 5. Exposition et Transfert des Contaminants | 6. Cartographie du Risque Écologique | Prochaines Étapes

1. Land cover of the study area | Study area | OCS-GE land cover | 2. Habitat layers | What is a habitat? | The habitat object | Adding layers one by one | The soil layer | From polygons to rasters | One stack for all layers | 3. Food web | 4. The spacemodel | 5. Dispersal | 6. Exposure | 7. Risk | Going further

Survival toxicity tests | Toxicokinetic-Toxicodynamic modeling | GUTS Modelling | GUTS-SD | GUTS-IT | For constant concentration exposure | Inference

Survival analysis using a toxicokinetic-toxicodynamic (TKTD) model: GUTS | GUTS model with constant exposure concentrations | GUTS model "SD" | Extract Nsurv | Fix background mortality | GUTS model "IT" | Computing prediction | Computing Nsurv on prediction | Removing background mortality in predictions | Validation criteria: EFSA recommendations | Computing predictions with number of survivors | Validation criteria | Plot and PPC of predict_Nsurv objects | Additional options for EFSA user | Lethal concentration | Multiplication factors: 'margin of safety' | Change the level of multiplication factor | Change the default time of multiplication factor assessment

DR modelling of binary data | Target-time DR analysis of binary data | Modelling | Inference | DR modelling of count data | Estimation of the effective observation period | Target-time DR analysis of count data | DR modelling of quantitative continuous data | Target-time DR analysis of quantitative continuous data

Binary data analysis | Step 1: check the structure and the dataset integrity | Step 2: create a BinaryData object | Step 3: visualise the raw data | Step 4: fit an exposure-response model to the survival data at target time | Plot fitting results | Step 5: the posterior predictive check as a goodness-of-fit criterion | Prior Posterior representation | Correlation plot | Goodness of Fit | Count data analysis | Model comparison | Count data versus binary data dedicated functions | Quantitative continuous data analysis | Load growth data | Dose-response plot of quantitative conitnuous data | Inference | Posterior Predictive Check (PPC)

Feel free to report any issue | Prior Posterior

Single Compartment and Single Exposure : Male Gammarus Single | Multiple Compartiment, Single Exposure - Default interaction | Multiple Compartiment, Single Exposure : Change nesting

A Simple Example: Male Gammarus Single | Data | Inference | Results | TK parameters | Male Gammarus Merged | Male Gammarus seanine with growth | Oncorhynchus | Chironomus benzo-a-pyrene | Prediction