update description and vignette YAML

DHARMa/DESCRIPTION

@@ -2,7 +2,7 @@ Package: DHARMa
 Title: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
 Version: 0.4.7
 Date: 2024-10-16
-Authors@R: c(person("Florian", "Hartig", email = "[email protected]", role = c("aut", "cre"), comment=c(ORCID="0000-0002-6255-9059")), person("Lukas", "Lohse", role = "ctb"))
+Authors@R: c(person("Florian", "Hartig", email = "[email protected]", role = c("aut", "cre"), comment=c(ORCID="0000-0002-6255-9059")), person("Lukas", "Lohse", role = "ctb"), person("Melina", "de Souza leite", role = "ctb"))
 Description: The 'DHARMa' package uses a simulation-based approach to create
     readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
     models. Currently supported are linear and generalized linear (mixed) models from 'lme4'

DHARMa/DHARMa.Rproj

@@ -18,4 +18,4 @@ StripTrailingWhitespace: Yes
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageRoxygenize: rd,collate,namespace
+PackageRoxygenize: rd,collate,namespace,vignette

DHARMa/vignettes/DHARMa.Rmd

@@ -3,12 +3,14 @@ title: "DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regres
 author: "Florian Hartig, Theoretical Ecology, University of Regensburg [website](https://www.uni-regensburg.de/biologie-vorklinische-medizin/theoretische-oekologie/mitarbeiter/hartig/)"
 date: "`r Sys.Date()`"
 output:
-  rmarkdown::html_vignette:
-    toc: true
   pdf_document:
     toc: true
-vignette: |
-  %\VignetteIndexEntry{Vignette for the DHARMa package} \usepackage[utf8]{inputenc} %\VignetteEngine{knitr::rmarkdown}
+  rmarkdown::html_vignette:
+    toc: true
+vignette: >
+  %\VignetteIndexEntry{Vignette for the DHARMa package} 
+  \usepackage[utf8]{inputenc} 
+  %\VignetteEngine{knitr::rmarkdown}
 abstract: "The 'DHARMa' package uses a simulation-based approach to create  readily
   interpretable scaled (quantile) residuals for fitted generalized linear (mixed)
   models. Currently supported are linear and generalized linear (mixed) models from

DHARMa/vignettes/DHARMaForBayesians.R

@@ -0,0 +1,78 @@
+## ----global_options, include=FALSE--------------------------------------------
+knitr::opts_chunk$set(fig.width=8.5, fig.height=5.5, fig.align='center', warning=FALSE, message=FALSE)
+
+## ----echo = F, message = F----------------------------------------------------
+library(DHARMa)
+set.seed(123)
+
+## ----eval = F-----------------------------------------------------------------
+#  library(rjags)
+#  library(BayesianTools)
+#  
+#  set.seed(123)
+#  
+#  dat <- DHARMa::createData(200, overdispersion = 0.2)
+#  
+#  Data = as.list(dat)
+#  Data$nobs = nrow(dat)
+#  Data$nGroups = length(unique(dat$group))
+#  
+#  modelCode = "model{
+#  
+#    for(i in 1:nobs){
+#      observedResponse[i] ~ dpois(lambda[i])  # poisson error distribution
+#      lambda[i] <- exp(eta[i]) # inverse link function
+#      eta[i] <- intercept + env*Environment1[i]  # linear predictor
+#    }
+#  
+#    intercept ~ dnorm(0,0.0001)
+#    env ~ dnorm(0,0.0001)
+#  
+#    # Posterior predictive simulations
+#    for (i in 1:nobs) {
+#      observedResponseSim[i]~dpois(lambda[i])
+#    }
+#  
+#  }"
+#  
+#  jagsModel <- jags.model(file= textConnection(modelCode), data=Data, n.chains = 3)
+#  para.names <- c("intercept","env", "lambda", "observedResponseSim")
+#  Samples <- coda.samples(jagsModel, variable.names = para.names, n.iter = 5000)
+#  
+#  x = BayesianTools::getSample(Samples)
+#  
+#  colnames(x) # problem: all the variables are in one array - this is better in STAN, where this is a list - have to extract the right columns by hand
+#  posteriorPredDistr = x[,3:202] # this is the uncertainty of the mean prediction (lambda)
+#  posteriorPredSim = x[,203:402] # these are the simulations
+#  
+#  sim = createDHARMa(simulatedResponse = t(posteriorPredSim), observedResponse = dat$observedResponse, fittedPredictedResponse = apply(posteriorPredDistr, 2, median), integerResponse = T)
+#  plot(sim)
+
+## ----eval=F-------------------------------------------------------------------
+#    # Posterior predictive simulations
+#    for (i in 1:nobs) {
+#      observedResponseSim[i]~dpois(lambda[i])
+#    }
+
+## ----eval = F-----------------------------------------------------------------
+#    for(i in 1:nobs){
+#      observedResponse[i] ~ dpois(lambda[i])  # poisson error distribution
+#      lambda[i] <- exp(eta[i]) # inverse link function
+#      eta[i] <- intercept + env*Environment1[i] + RE[group[i]] # linear predictor
+#    }
+#  
+#    for(j in 1:nGroups){
+#     RE[j] ~ dnorm(0,tauRE)
+#    }
+
+## ----eval=F-------------------------------------------------------------------
+#    for(j in 1:nGroups){
+#     RESim[j] ~ dnorm(0,tauRE)
+#    }
+#  
+#    for (i in 1:nobs) {
+#      observedResponseSim[i] ~ dpois(lambdaSim[i])
+#      lambdaSim[i] <- exp(etaSim[i])
+#      etaSim[i] <- intercept + env*Environment1[i] + RESim[group[i]]
+#    }
+