integrated supp file

_quarto.yml

@@ -1,5 +1,7 @@
 project:
   type: website
   title: "published-202312-favrot-hierarchical"
+  render: 
+    - published-202312-favrot-hierarchical.qmd
 
 

published-202312-favrot-hierarchical-supp.qmd

@@ -7,8 +7,7 @@ format: pdf
 library(ggplot2)
 ```
 
-
-# Model adjustment and comparison to the negative binomial model
+## Model adjustment and comparison to the negative binomial model { .unnumbered } 
 
 To check the model's fit to the data, we performed a posterior predictive check of our model to check that the data were compatible with the model assumptions. To do so, we computed the probability of exceeding each individual data with the fitted model (2). Note that the number of pest individuals per plant are not available in practice; the data correspond to observed numbers of pest individuals for groups of $N_i$ plants. Based on the posterior probability check, the computed probabilities were all falling in the range 0.22-0.93 (except for the observations equal to 0, for which the probability of being greater was equal to 1), and were thus not extreme. This result indicates that the model specified is not incompatible with the observed data and that the over-dispersion was correctly taken into account.
 

published-202312-favrot-hierarchical.qmd

@@ -814,27 +814,34 @@ We successfully apply the model to a real dataset including prevalence and incid
 Results obtained from simulated data confirm that, when pest prevalence and pest intensity are collected separately in different trials, the model parameters are more accurately estimated combining both prevalence and intensity trials than using one type of trials only. We also find that, when prevalence data are collected in all trials and intensity data are collected in a subset of trials, estimations and pest treatment ranking are more accurate using both types of data than using prevalence data only. Moreover, when only one type of observation can be collected in a pest survey or in an experimental trial, our analysis indicates that it is usually better to collect intensity data than prevalence data, especially in situations where all or most of the plants are expected to be infested. Finally, our simulations show that it is unlikely to obtain accurate results with fewer than 40 trials when assessing the efficacy of pest control treatments based on prevalence and intensity data.
   
 Although our framework is illustrated to compare the efficacy of plant pest treatments, it could be applied to other areas of research in the future, in particular for optimizing designs used in animal and human epidemiology. It is imperative to note that the ultimate selection of a design should be contingent upon the consideration of local constraints. As the model codes are made fully available, we believe that these codes could be used by different institutes to compare many different designs in the future, not only the types of designs considered in our paper. Of particular significance is the capability of our model to optimize sample sizes, with its impact contingent on the relative importance of within-trial variability compared to between-trial variability.    
-   
-# Author contributions {.unnumbered}
+
+# Supplementary material { .unnumbered }
+
+{{< include published-202312-favrot-hierarchical-supp.qmd >}}
+
+# Author contributions {.appendix }
 
 AF and DM designed the study. AF performed the computations. AF and DM wrote the paper.
 
-# Funding {.unnumbered}
+# Funding {.appendix}
 
 This work was partly funded by the project SEPIM (PNRI) and by the RMT SDMAA. 
     
-# Data availability {.unnumbered}
+# Data availability {.appendix }
     
 Simulated data and model parameters are available without restriction. The original experimental data may be available upon request.
 
-# Acknowledgements {.unnumbered}
+# Acknowledgements {.appendix }
 
 We are grateful to Anabelle Laurent, Elma Raaijmakers, Kathleen Antoons and to the institute ITB (https://www.itbfr.org/) for their comments on this project. 
 
 We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10.15454/1.5572390655343293E12) for providing help and/or computing and/or storage resources.
 
 The authors are thankful to the institutes that provided us with the data, namely the French Institut Technique de la Betterave, the sugar beet organisation of the Netherlands, and the Institut Royal Belge pour l'Amélioration de la Betterave.
 
-# References {.unnumbered} 
 
 
+# References {.unnumbered} 
+
+::: {#refs}
+:::