Merge remote-tracking branch 'upstream/master'

.Rbuildignore

@@ -24,3 +24,6 @@
 ^doc$
 ^Meta$
 ^_pkgdown.yml$
+^pkgdown$
+^index.md$
+

.gitlab-ci.yml

@@ -141,7 +141,7 @@ buildDocker:
   after_script:
     - docker logout $CI_REGISTRY
   rules:
-    - if: $CI_COMMIT_BRANCH == "master"
+    - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
 
 deploy:
   image: debian
@@ -150,7 +150,7 @@ deploy:
     name: shinyproxy
     url: https://shiny.biosp.inrae.fr/
   rules:
-    - if: $CI_COMMIT_BRANCH == "master"
+    - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
   before_script:
     - apt-get update && apt-get install -y sshpass openssh-client
     - export SSHPASS=$DEPLOY_PWD
@@ -171,7 +171,5 @@ pages:
     paths:
       - public
   rules:
-    - if: '$CI_COMMIT_BRANCH == "master"'
+    - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
       when: always
-
-

DESCRIPTION

@@ -2,16 +2,26 @@ Package: landsepi
 Type: Package
 Encoding: UTF-8
 Title: Landscape Epidemiology and Evolution
-Version: 1.0.2
-Date: 2020-11-26
-Authors@R: c(person("Loup", "Rimbaud", role = "aut", email = "loup.rimbaud@inrae.fr"),
-        person("Julien", "Papaix", role = "aut", email = "julien.papaix@inrae.fr"),
-        person("Jean-Francois", "Rey", role = "cre", email = "jean-francois.rey@inrae.fr"),
-        person("Jean-Loup", "Gaussen", role = "ctb", email = "jean-loup-thomas.gaussen@inrae.fr"))
-Author: Loup Rimbaud [aut],
-    Julien Papaix [aut],
-    Jean-Francois Rey [cre],
-    Jean-Loup Gaussen [ctb]
+Version: 1.1.0
+Date: 2021-07-19
+Authors@R:
+    c(person(given = "Loup",
+             family = "Rimbaud",
+             role = "aut",
+             email = "loup.rimbaud@inrae.fr"),
+      person(given = "Julien",
+             family = "Papaix",
+             role = "aut",
+             email = "julien.papaix@inrae.fr"),
+      person(given = "Jean-François",
+             family = "Rey",
+             role = c("aut","cre"),
+             email = "jean-francois.rey@inrae.fr",
+             comment = c(ORCID = "0000-0003-3281-6701")),
+      person(given = "Jean-Loup",
+             family = "Gaussen",
+             role = "ctb",
+             email = "jean-loup-thomas.gaussen@inrae.fr"))
 Maintainer: Jean-Francois Rey <jean-francois.rey@inrae.fr>
 Description: A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution 
     of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. 
@@ -22,7 +32,9 @@ Description: A stochastic, spatially-explicit, demo-genetic model simulating the
     respect to their epidemiological, evolutionary and economic outcomes.
     Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
     Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
-URL: https://gitlab.paca.inrae.fr/CSIRO-INRA/landsepi
+URL: https://csiro-inra.pages.biosp.inrae.fr/landsepi/,
+     https://gitlab.paca.inra.fr/CSIRO-INRA/landsepi
+
 BugReports: https://gitlab.paca.inrae.fr/CSIRO-INRA/landsepi/-/issues
 License: GPL (>= 2) | file LICENSE
 LazyData: true

R/AgriLand.R

@@ -42,7 +42,8 @@
 #' "rotation_sequence" are associated with the same level of aggregation.
 #' @param algo the algorithm used for the computation of the variance-covariance matrix of the multivariate
 #' normal distribution: "exp" for exponential function, "periodic" for periodic function,
-#' "random" for random draw (see details of function multiN). If algo="random", the parameter aggreg is not used.
+#' "random" for random draw (see details of function multiN). If algo="random", the parameter aggreg is not used. 
+#' Algorithm "exp" is preferable for big landscapes.
 #' @param croptype_names a vector of croptype names (for legend in graphic).
 #' @param graphic a logical indicating if a graphic of the landscape must be generated (TRUE) or not (FALSE).
 #' @param outputDir a directory to save graphic

R/GPKGTools.R

@@ -128,7 +128,7 @@ GPKGAddTables <- function(gpkgfile) {
                                                      yield_L REAL NOT NULL CHECK(yield_L >= 0),
                                                      yield_I REAL NOT NULL CHECK(yield_I >= 0),
                                                      yield_R REAL NOT NULL CHECK(yield_R >= 0),
-                                                     production_cost REAL NOT NULL CHECK(production_cost >= 0),
+                                                     planting_cost REAL NOT NULL CHECK(planting_cost >= 0),
                                                      market_value REAL NOT NULL CHECK(market_value >= 0));"
   )
 

R/Methods-LandsepiParams.R

@@ -22,7 +22,7 @@
 .croptypesColNames <- c("croptypeID", "croptypeName")
 .cultivarsColNames <- c("cultivarName", "initial_density", "max_density", "growth_rate"
                         , "reproduction_rate", "death_rate", "yield_H", "yield_L", "yield_I"
-                        , "yield_R", "production_cost", "market_value")
+                        , "yield_R", "planting_cost", "market_value")
 .cultivarsGenesColNames <- c()
 .geneColNames <- c("geneName", "efficiency", "time_to_activ_exp", "time_to_activ_var"
                    , "mutation_prob", "Nlevels_aggressiveness", "fitness_cost"
@@ -465,7 +465,7 @@ saveDeploymentStrategy <- function(params, outputGPKG = "landsepi_landscape.gpkg
 #' @param writeTXT a logical indicating if outputs must be written in text files (TRUE, default) 
 #' or not (FALSE).
 #' @param videoMP4 a logical indicating if a video must be generated (TRUE) or not (FALSE, default).
-#' Works only if graphic=TRUE and audpc is computed.
+#' Works only if graphic=TRUE and audpc_rel is computed.
 #' @param keepRawResults a logical indicating if binary files must be kept after the end of 
 #' the simulation (default=FALSE). Careful, many files may be generated if keepRawResults=TRUE.
 #' @details See ?landsepi for details on the model, assumptions and outputs, and our vignettes 
@@ -559,8 +559,24 @@ runSimul <- function(params, graphic=TRUE, writeTXT=TRUE, videoMP4=FALSE, keepRa
   initPath <- getwd()
   setwd(params@OutputDir)
 
+  ## remove genes not used from CultivarsGenes and Genes
+  if(ncol(params@CultivarsGenes) >= 1) {
+    drop_genes <- lapply(1:ncol(params@CultivarsGenes), FUN = function(c) {
+      if(sum(params@CultivarsGenes[,c]) == 0) return(c);
+    })
+    drop_genes <- which(!sapply(drop_genes,is.null))
+  } else { drop_genes <- NULL }
+
+  if( !is.null(drop_genes) && length(drop_genes) > 0){
+    print(paste0("Genes not affected ",params@Genes[drop_genes,1]))
+    cultivarsGenes_tmp <- params@CultivarsGenes[,-drop_genes]  
+    Genes_tmp <- params@Genes[-drop_genes,]
+  } else {
+    cultivarsGenes_tmp <- params@CultivarsGenes
+    Genes_tmp <- params@Genes 
+  }
   cultivars_genes_list <- lapply(1:nrow(params@Cultivars), FUN = function(i) {
-    return(which(params@CultivarsGenes[i, ] == 1) - 1)
+    return(which(cultivarsGenes_tmp[i, ] == 1) - 1)
   })
 
   cdf <- as.data.frame(params@Landscape)
@@ -576,7 +592,7 @@ runSimul <- function(params, graphic=TRUE, writeTXT=TRUE, videoMP4=FALSE, keepRa
     croptype_names = params@Croptypes$croptypeName,
     cultivars = params@Cultivars,
     cultivars_genes_list = cultivars_genes_list,
-    genes = params@Genes,
+    genes = Genes_tmp,
     landscape = as_Spatial(st_geometry(params@Landscape)),
     area = as.vector(params@Landscape$area[, 1]),
     rotation = rotation,
@@ -723,7 +739,8 @@ setLandscape <- function(params, land) {
   params@Landscape$area <- data.frame(area = st_area(params@Landscape))
   
   ## Initialise host and pathogen dispersal with diagonal matrices
-  if (length(params@DispHost) == 0){
+  if (length(params@DispHost) == 0 |
+      length(params@DispHost) != (nrow(params@Landscape))^2){
     disp_host <- loadDispersalHost(params, type = "no")
     params <- setDispersalHost(params, disp_host)
   }
@@ -1007,7 +1024,8 @@ checkDispersalHost <- function(params) {
 #' @param algo the algorithm used for the computation of the variance-covariance matrix 
 #' of the multivariate normal distribution: "exp" for exponential function, "periodic" 
 #' for periodic function, "random" for random draw (see details of function multiN). 
-#' If algo="random", the parameter aggreg is not used.
+#' If algo="random", the parameter aggreg is not used. 
+#' Algorithm "exp" is preferable for big landscapes.
 #' @param graphic a logical indicating if graphics must be generated (TRUE) or not (FALSE).
 #' @details An algorithm based on latent Gaussian fields is used to allocate two different croptypes 
 #' across the simulated landscapes (e.g. a susceptible and a resistant cultivar, denoted as 
@@ -1143,7 +1161,7 @@ loadPathogen <- function(disease = "rust") {
 #' for a pathogen genotype not adapted to resistance: \itemize{
 #' \item infection_rate = maximal expected infection rate of a propagule on a healthy host,
 #' \item propagule_prod_rate = maximal expected effective propagule production rate of an 
-#' infectious host per timestep,
+#' infectious host per time step,
 #' \item latent_period_exp = minimal expected duration of the latent period,
 #' \item latent_period_var = variance of the latent period duration,
 #' \item infectious_period_exp = maximal expected duration of the infectious period,
@@ -1508,7 +1526,7 @@ loadCultivar <- function(name, type = "growingHost") {
       "yield_L" = 0.0,
       "yield_I" = 0.0,
       "yield_R" = 0.0,
-      "production_cost" = 225,
+      "planting_cost" = 225,
       "market_value" = 200
     ),
     "nongrowingHost" = list(
@@ -1522,7 +1540,7 @@ loadCultivar <- function(name, type = "growingHost") {
       "yield_L" = 0.0,
       "yield_I" = 0.0,
       "yield_R" = 0.0,
-      "production_cost" = 225,
+      "planting_cost" = 225,
       "market_value" = 200
     ),
     "nonCrop" = list(
@@ -1536,7 +1554,7 @@ loadCultivar <- function(name, type = "growingHost") {
       "yield_L" = 0.0,
       "yield_I" = 0.0,
       "yield_R" = 0.0,
-      "production_cost" = 0,
+      "planting_cost" = 0,
       "market_value" = 0
     ),
     list()
@@ -1565,31 +1583,32 @@ loadCultivar <- function(name, type = "growingHost") {
 #' @details dfCultivars is a dataframe of parameters associated with each host genotype 
 #' (i.e. cultivars, lines) when cultivated in pure crops. Columns of the dataframe are:\itemize{
 #' \item cultivarName: cultivar names (cannot accept space),
-#' \item initial_density: host densities (per square meter) at the beginning of the cropping season,
-#' \item max_density: maximum host densities (per square meter) at the end of the cropping season,
+#' \item initial_density: host densities (per square meter) at the beginning of the cropping season 
+#' as if cultivated in pure crop,
+#' \item max_density: maximum host densities (per square meter) at the end of the cropping season 
+#' as if cultivated in pure crop,
 #' \item growth rate: host growth rates,
 #' \item reproduction rate: host reproduction rates,
 #' \item death rate: host death rates,
-#' \item yield_H: yield (in weight or volume units / ha / cropping season) 
-#' associated with hosts in sanitary status H,
-#' \item yield_L: yield (in weight or volume units / ha / cropping season) 
-#' associated with hosts in sanitary status L,
-#' \item yield_I: yield (in weight or volume units / ha / cropping season) 
-#' associated with hosts in sanitary status I,
-#' \item yield_R: yield (in weight or volume units / ha / cropping season) 
-#' associated with hosts in sanitary status R,
-#' \item production_cost = overall production costs (in monetary units / ha / cropping season)
-#' including planting costs, amortisation, labour etc.,
-#' \item market_value = market values of the productions (in monetary units / weight or volume unit).
+#' \item yield_H: theoretical yield (in weight or volume units / ha / cropping season) 
+#' associated with hosts in sanitary status H as if cultivated in pure crop,
+#' \item yield_L: theoretical yield (in weight or volume units / ha / cropping season) 
+#' associated with hosts in sanitary status L as if cultivated in pure crop,
+#' \item yield_I: theoretical yield (in weight or volume units / ha / cropping season) 
+#' associated with hosts in sanitary status I as if cultivated in pure crop,
+#' \item yield_R: theoretical yield (in weight or volume units / ha / cropping season) 
+#' associated with hosts in sanitary status R as if cultivated in pure crop,
+#' \item planting_cost = planting costs (in monetary units / ha / cropping season) as if cultivated in pure crop,
+#' \item market_value = market values of the production (in monetary units / weight or volume unit).
 #' }
 #' 
 #' The data.frame must be defined as follow (example):
 #' 
-#' | cultivarName | initial_density | max_density | growth_rate | reproduction_rate | death_rate | yield_H | yield_L | yield_I |yield_R | production_cost | market_value |
-#' | ------------ | --------------- | ----------- | ----------- | ----------------- | ---------- | ------- | ------- | ------- | ------ | --------------- | ------------ |
-#' | Susceptible  | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225             | 200          |
-#' | Resistant1   | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225             | 200          |
-#' | Resistant2   | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225             | 200          |
+#' | cultivarName | initial_density | max_density | growth_rate | reproduction_rate | death_rate | yield_H | yield_L | yield_I |yield_R | planting_cost | market_value |
+#' | ------------ | --------------- | ----------- | ----------- | ----------------- | ---------- | ------- | ------- | ------- | ------ | ------------- | ------------ |
+#' | Susceptible  | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225           | 200          |
+#' | Resistant1   | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225           | 200          |
+#' | Resistant2   | 0.1             |  2.0        | 0.1         | 0.0               | 0.0        | 2.5     | 0.0     | 0.0     | 0.0    | 225           | 200          |
 #'
 #' @return a LandsepiParams object
 #' @seealso \link{loadCultivar}
@@ -1659,7 +1678,7 @@ checkCultivars <- function(params) {
      !is.numeric(params@Cultivars$yield_L) ||
      !is.numeric(params@Cultivars$yield_I) ||
      !is.numeric(params@Cultivars$yield_R) ||
-     !is.numeric(params@Cultivars$production_cost) ||
+     !is.numeric(params@Cultivars$planting_cost) ||
      !is.numeric(params@Cultivars$market_value) ||
      
      sum( !is.positive(params@Cultivars$initial_density) ) > 0 ||
@@ -1667,9 +1686,9 @@ checkCultivars <- function(params) {
      sum( !is.positive(params@Cultivars$yield_L) ) > 0 ||
      sum( !is.positive(params@Cultivars$yield_I) ) > 0 ||
      sum( !is.positive(params@Cultivars$yield_R) ) > 0 ||
-     sum( !is.positive( params@Cultivars$production_cost) ) > 0 ||
+     sum( !is.positive( params@Cultivars$planting_cost) ) > 0 ||
      sum( !is.positive(params@Cultivars$market_value) ) > 0 ){
-    warning("initial_density, yield, production_cost and market_value must be >= 0")
+    warning("initial_density, yield, planting_cost and market_value must be >= 0")
     ret <- FALSE
   }
   
@@ -2029,15 +2048,17 @@ checkInoculum <- function(params) {
 #' @param epid_outputs a character string (or a vector of character strings if several outputs 
 #' are to be computed) specifying the type of epidemiological and economic outputs to generate 
 #' (see details):\itemize{
-#' \item "audpc" : Area Under Disease Progress Curve (average proportion of diseased hosts relative 
-#' to the carryng capacity) 
-#' \item "gla_abs" : Absolute Green Leaf Area (average number of healthy hosts per square meter)
-#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy hosts relative to the 
+#' \item "audpc" : Area Under Disease Progress Curve (average number of diseased host individuals
+#' per time step and square meter) 
+#' \item "audpc_rel" : Relative Area Under Disease Progress Curve (average proportion of diseased host
+#' individuals relative to the total number of existing hosts)
+#' \item "gla" : Green Leaf Area (average number of healthy host individuals per time step and square meter)
+#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy host individuals relative to the 
 #' total number of existing hosts)
-#' \item "eco_cost" : total crop costs (in weight or volume units per ha) 
-#' \item "eco_product" : total crop production (in monetary units per ha) 
-#' \item "eco_benefit" : total crop benefits (in monetary units per ha) 
-#' \item "eco_grossmargin" : Gross Margin (benefits - costs, in monetary units per ha) 
+#' \item "eco_yield" : total crop yield (in weight or volume units per ha) 
+#' \item "eco_cost" : operational crop costs (in monetary units per ha) 
+#' \item "eco_product" : total crop products (in monetary units per ha) 
+#' \item "eco_margin" : Margin (products - operational costs, in monetary units per ha) 
 #' \item "HLIR_dynamics", "H_dynamics", "L_dynamics", "IR_dynamics", "HLI_dynamics", etc.: 
 #' Epidemic dynamics related to the specified sanitary status (H, L, I or R and all their 
 #' combinations). Graphics only, works only if graphic=TRUE.
@@ -2063,7 +2084,7 @@ loadOutputs <- function(epid_outputs = "all", evol_outputs = "all"){
                      , evol_outputs = evol_outputs
                      , thres_breakdown = 50000
                      , GLAnoDis = 1.48315
-                     , audpc100S = 0.38)
+                     , audpc100S = 0.76)
   return(outputList)
 }
 
@@ -2084,21 +2105,23 @@ loadOutputs <- function(epid_outputs = "all", evol_outputs = "all"){
 #' \item GLAnoDis = the absolute Green Leaf Area in absence of disease (used to compute 
 #' economic outputs).
 #' \item audpc100S = the audpc in a fully susceptible landscape (used as reference value 
-#' for graphics and video).
+#' for graphics).
 #' }
 #' @details "epid_outputs" is a character string (or a vector of character strings if several 
 #' outputs are to be computed) specifying the type of epidemiological and economic outputs 
 #' to generate:  
 #' \itemize{
-#' \item "audpc" : Area Under Disease Progress Curve (average proportion of diseased hosts relative 
-#' to the carryng capacity) 
-#' \item "gla_abs" : Absolute Green Leaf Area (average number of healthy hosts per square meter)
-#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy hosts relative to the 
+#' \item "audpc" : Area Under Disease Progress Curve (average number of diseased host individuals
+#' per time step and square meter) 
+#' \item "audpc_rel" : Relative Area Under Disease Progress Curve (average proportion of diseased host
+#' individuals relative to the total number of existing hosts)
+#' \item "gla" : Green Leaf Area (average number of healthy host individuals per square meter)
+#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy host individuals relative to the 
 #' total number of existing hosts)
-#' \item "eco_cost" : total crop costs (in weight or volume units per ha) 
-#' \item "eco_product" : total crop production(in monetary units per ha) 
-#' \item "eco_benefit" : total crop benefits (in monetary units per ha) 
-#' \item "eco_grossmargin" : Gross Margin (benefits - costs, in monetary units per ha) 
+#' \item "eco_yield" : total crop yield (in weight or volume units per ha) 
+#' \item "eco_cost" : operational crop costs (in monetary units per ha) 
+#' \item "eco_product" : total crop products (in monetary units per ha) 
+#' \item "eco_margin" : Margin (products - costs, in monetary units per ha) 
 #' \item "HLIR_dynamics", "H_dynamics", "L_dynamics", "IR_dynamics", "HLI_dynamics", etc.: 
 #' Epidemic dynamics related to the specified sanitary status (H, L, I or R and all their 
 #' combinations). Graphics only, works only if graphic=TRUE.

R/RcppExports.R

@@ -118,7 +118,7 @@
 #'              L = as.numeric(cultivars$yield_L),
 #'              I = as.numeric(cultivars$yield_I),
 #'              R = as.numeric(cultivars$yield_R)),
-#'              production_cost_perHa = as.numeric(cultivars$production_cost),
+#'              planting_cost_perHa = as.numeric(cultivars$planting_cost),
 #'              market_value = as.numeric(cultivars$market_value))
 #'             
 #' evol_res <- evol_output(, time_param, Npoly, cultivars, genes)

R/demo_landsepi.R

@@ -128,7 +128,7 @@ demo_landsepi <- function(seed = 12345, strat = "MO", Nyears = 20, nTSpY = 120,
   #                         yield_L =           rep(0.0, 3),
   #                         yield_I =           rep(0.0, 3),
   #                         yield_R =           rep(0.0, 3),
-  #                         production_cost =   rep(225, 3),
+  #                         planting_cost =   rep(225, 3),
   #                         market_value =      rep(200, 3),
   #                         stringsAsFactors = FALSE)
   simul_params <- setCultivars(simul_params, cultivars)

R/graphics.R

@@ -75,13 +75,16 @@ plotland <- function(landscape, COL = rep(0, length(landscape)), DENS = rep(0, l
   }
   if (LEGEND1[1] != "") {
     if (TITLE.LEG2 == "") {
-      legend(bounds$xmax / 2.66, -bounds$ymax / 40, legend = LEGEND1, fill = COL.LEG, bty = "n")
+      legend(bounds$xmin + (bounds$xmax-bounds$xmin) / 2.66, bounds$ymin - (bounds$ymax-bounds$ymin) / 40, 
+             # bounds$xmax / 2.66, -bounds$ymax / 40, 
+             # mean(c(bounds$xmin, bounds$xmax)), bounds$ymin,
+             legend = LEGEND1, fill = COL.LEG, bty = "n")
     } else {
-      legend(bounds$xmax / 2.66, -bounds$ymax / 40,
-        legend = LEGEND1, col = "black", density = 2 * DENS.LEG, angle = ANGLE.LEG,
-        bty = "n"
-      )
-      legend(-bounds$xmax / 5, bounds$ymax, legend = LEGEND2, fill = COL.LEG, bty = "n", title = TITLE.LEG2)
+      legend(bounds$xmin + (bounds$xmax - bounds$xmin) / 2.66, bounds$ymin - (bounds$ymax-bounds$ymin) / 40, 
+             # bounds$xmax / 2.66, -bounds$ymax / 40,
+             legend = LEGEND1, col = "black", density = 2 * DENS.LEG, angle = ANGLE.LEG, bty = "n")
+      legend(bounds$xmin - (bounds$xmax-bounds$xmin) / 5, bounds$ymax
+             , legend = LEGEND2, fill = COL.LEG, bty = "n", title = TITLE.LEG2)
     }
   }
 }

R/landsepi.R

@@ -37,8 +37,8 @@
 #' @details \tabular{ll}{
 #'          Package: \tab landsepi\cr
 #'          Type: \tab Package\cr
-#'          Version: \tab 1.0.2\cr
-#'          Date: \tab 2020-11-26\cr
+#'          Version: \tab 1.1.0\cr
+#'          Date: \tab 2021-07-19\cr
 #'          License: \tab GPL (>=2)\cr
 #'          }
 #'
@@ -52,7 +52,8 @@
 #' potential bottlenecks to the pathogen.
 #'
 #' The lansdcape is represented by a set of polygons where the pathogen can disperse
-#' (the basic spatial unit is an individual field). *landsepi* includes built-in simulated landscapes 
+#' (the basic spatial unit is an individual polygon; an agricultural field may be composed of a single 
+#' or several polygons). *landsepi* includes built-in simulated landscapes 
 #' (and associated dispersal matrices for rust pathogens, see below), but is it possible 
 #' to use your own landscape (in shapefile format) and dispersal matrix.  
 #' 
@@ -98,9 +99,10 @@
 #' \describe{
 #' \item{\strong{Assumptions} (in bold those that can be relaxed with appropriate parameterization): }{
 #' \enumerate{
-#'  \item The spatial unit is the field, i.e. a piece of land delimited by boundaries and possibly cultivated with a crop.
-#'  Such crop may be host or non-host, and the field is considered a homogeneous mixture of individuals (i.e. there is no
-#'  intra-field structuration).
+#'  \item The spatial unit is a polygon, i.e. a piece of land delimited by boundaries and possibly 
+#'  cultivated with a crop. Such crop may be host or non-host, and the polygon is considered a homogeneous 
+#'  mixture of host individuals (i.e. there is no intra-polygon structuration). A field may be composed 
+#'  of a single or several polygons..
 #'  \item Host individuals are in one of these four categories: H (healthy), 
 #'  E (latent, i.e. infected but not infectious nor symptomatic), I (infectious and symptomatic), 
 #'  or R (removed, i.e. epidemiologically inactive). 
@@ -116,6 +118,9 @@
 #'  susceptible to disease from the first to the last day of every cropping season.
 #'  \item Crop yield depends on the average amount of producing host individuals during the cropping season 
 #'  and does not depend on the time of epidemic peak. **Only healthy individuals (state H) contribute to crop yield.**
+#'  \item Components of a mixture are independent each other (i.e. there is neither plant-plant interaction 
+#'  nor competition for space, and harvests are segregated). 
+#'  \item The pathogen is haploid.
 #'  \item Initially, the pathogen is not adapted to any source of resistance, and is only present on
 #'  susceptible hosts (at state I).
 #'  \item **Pathogen dispersal is isotropic (i.e. equally probable in every direction).**
@@ -129,19 +134,22 @@
 #'  \item When there is a delay for activation of a given resistance gene (APR), the time to activation is the same for
 #'  all hosts carrying this gene and located in the same field.
 #'  \item Variances of the durations of the latent and the infectious periods of the pathogen are not affected by plant resistance.
-#'  \item If there is sexual reproduction (or gene recombination), it occurs only between parental infections located in the same field
-#'  and the same host genotype. The propagule production rate of a couple is the sum of the propagule production rates of the parents.
-#'  The genotype of each daughter propagule is issued from random loci segregation between parental loci.
+#'  \item If there is sexual reproduction (or gene recombination), it occurs only between parental infections located 
+#'  in the same polygon and the same host genotype. The host population is panmictic (i.e. all pairs of parents have 
+#'  the same probability to occur). The propagule production rate of a couple is the sum of the propagule production 
+#'  rates of the parents. The genotype of each daughter propagule is issued from random loci segregation between parental loci.
 #'   }
 #'  }
 
 #' \item{\strong{Epidemiological outputs}}{
 #' The epidemiological outcome of a deployment strategy is evaluated using: \enumerate{
 #' \item the area under the disease progress curve (AUDPC) to measure disease severity
-#' (i.e. the average proportion of diseased hosts -status I and R- relative to the carrying capacity),
-#' \item the absolute Green Leaf Area (GLAa) to measure the average amount of healthy tissue (status H),
+#' (i.e. the average number of diseased plant tissue -status I and R- per time step and square meter),
+#' \item the relative area under the disease progress curve (AUDPCr) to measure the average proportion 
+#' of diseased tissue (status I and R) relative to the total number of existing host individuals (H+L+I+R).
+#' \item the Green Leaf Area (GLA) to measure the average amount of healthy plant tissue (status H) per time step and square meter,
 #' \item the relative Green Leaf Area (GLAr) to measure the average proportion of healthy tissue (status H)
-#' relative to the total number of existing hosts (H+L+I+R).
+#' relative to the total number of existing host individuals (H+L+I+R).
 #'   }
 #' A set of graphics and a video showing epidemic dynamics can also be generated.
 #'  }
@@ -159,13 +167,13 @@
 
 #' \item{\strong{Economic outputs}}{
 #' The economic outcome of a simulation can be evaluated using: \enumerate{
-#' \item the crop production: yearly crop production (e.g. grains, fruits, wine) in weight (or volume) units
-#' per hectare (depends on the number of productive hosts and associated yield),
-#' \item the crop benefits: yearly benefits generated from product sales, in monetary units per hectare
-#' (depends on crop production and market value of the product),
-#' \item the crop costs: yearly costs associated with crop production (including planting, amortisation, labour, ...)
-#'  in monetary units per hectare (depends on initial host density and production cost),
-#' \item the gross margin, i.e. benefits - costs, in monetary units per hectare.
+#' \item the crop yield: yearly crop production (e.g. grains, fruits, wine) in weight (or volume) units
+#' per hectare (depends on the number of productive hosts and associated theoretical yield),
+#' \item the crop products: yearly products generated from sales, in monetary units per hectare
+#' (depends on crop yield and market value),
+#' \item the crop operational costs: yearly costs associated with crop planting, 
+#' in monetary units per hectare (depends on initial host density and planting cost),
+#' \item the margin, i.e. products - operational costs, in monetary units per hectare.
 #'   }
 #'  }
 #' }

R/runShiny.R

@@ -10,9 +10,9 @@ runShinyApp <- function() {
     stop("Could not find example directory. Try re-installing `landsepi`.", call. = FALSE)
   }
   
-  needed_packages <- c("shiny","DT", "shinyjs", "gridExtra", "png", "grid", "future", "promises", "tools")
+  needed_packages <- c("shiny", "shinyBS", "shinyalert", "DT", "shinyjs", "gridExtra", "png", "grid", "future", "promises", "tools")
   if( sum(needed_packages %in% utils::installed.packages()[,1] == FALSE) != 0) {
-    stop('Install packages : install.packages(c("shiny","DT", "shinyjs", "gridExtra", "png", "grid", "future", "promises", "tools"))')
+    stop('Install packages : install.packages(c("shiny","shinyBS", "shinyalert", "DT", "shinyjs", "gridExtra", "png", "grid", "future", "promises", "tools"))')
   } 
 
   shiny::runApp(appDir, launch.browser = TRUE, display.mode = "normal")

R/simul_landsepi.R

@@ -33,18 +33,23 @@
 #' @param cultivars a dataframe of parameters associated with each host genotype (i.e. cultivars)
 #' when cultivated in pure crops. Columns of the dataframe are:\itemize{
 #' \item cultivarName: cultivar names,
-#' \item initial_density: host densities (per square meter) at the beginning of the cropping season,
-#' \item max_density: maximum host densities (per square meter) at the end of the cropping season,
-#' \item growth rate: host growth rates,
+#' \item initial_density: host densities (per square meter) at the beginning of the cropping season 
+#' as if cultivated in pure crop,
+#' \item max_density: maximum host densities (per square meter) at the end of the cropping season 
+#' as if cultivated in pure crop,
+#' \item growth_rate: host growth rates,
 #' \item reproduction rate: host reproduction rates,
-#' \item death rate: host death rates,
-#' \item yield_H: yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status H,
-#' \item yield_L: yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status L,
-#' \item yield_I: yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status I,
-#' \item yield_R: yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status R,
-#' \item production_cost = overall production costs (in monetary units / ha / cropping season)
-#' including planting costs, amortisation, labour etc.,
-#' \item market_value = market values of the productions (in monetary units / weight or volume unit).
+#' \item death_rate: host death rates,
+#' \item yield_H: theoretical yield (in weight or volume units / ha / cropping season) associated with 
+#' hosts in sanitary status H as if cultivated in pure crop,
+#' \item yield_L: theoretical yield (in weight or volume units / ha / cropping season) associated with 
+#' hosts in sanitary status L as if cultivated in pure crop,
+#' \item yield_I: theoretical yield (in weight or volume units / ha / cropping season) associated with 
+#' hosts in sanitary status I as if cultivated in pure crop,
+#' \item yield_R: theoretical yield (in weight or volume units / ha / cropping season) associated with 
+#' hosts in sanitary status R as if cultivated in pure crop,
+#' \item planting_cost = planting costs (in monetary units / ha / cropping season) as if cultivated in pure crop,
+#' \item market_value = market values of the production (in monetary units / weight or volume unit).
 #' }
 #' @param cultivars_genes_list a list containing, for each host genotype, the indices of carried resistance genes.
 #' @param genes a data.frame of parameters associated with each resistance gene and with the evolution of
@@ -71,7 +76,7 @@
 #' @param basic_patho_param a list of pathogen aggressiveness parameters on a susceptible host
 #' for a pathogen genotype not adapted to resistance: \itemize{
 #' \item infection_rate = maximal expected infection rate of a propagule on a healthy host,
-#' \item propagule_prod_rate = maximal expected effective propagule production rate of an infectious host per timestep,
+#' \item propagule_prod_rate = maximal expected effective propagule production rate of an infectious host per time step,
 #' \item latent_period_exp = minimal expected duration of the latent period,
 #' \item latent_period_var = variance of the latent period duration,
 #' \item infectious_period_exp = maximal expected duration of the infectious period,
@@ -91,15 +96,17 @@
 #' @param epid_outputs a character string (or a vector of character strings if several outputs are to be computed)
 #' specifying the type of epidemiological and economic outputs to generate (see details):  
 #' \itemize{
-#' \item "audpc" : Area Under Disease Progress Curve (average proportion of diseased hosts relative 
-#' to the carryng capacity) 
-#' \item "gla_abs" : Absolute Green Leaf Area (average number of healthy hosts per square meter)
-#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy hosts relative to the 
+#' \item "audpc" : Area Under Disease Progress Curve (average number of diseased host individuals
+#' per time step and square meter) 
+#' \item "audpc_rel" : Relative Area Under Disease Progress Curve (average proportion of diseased host
+#' individuals relative to the total number of existing hosts)
+#' \item "gla" : Green Leaf Area (average number of healthy host individuals per time step and square meter)
+#' \item "gla_rel" : Relative Green Leaf Area (average proportion of healthy host individuals relative to the 
 #' total number of existing hosts)
-#' \item "eco_cost" : total crop costs (in weight or volume units per ha) 
-#' \item "eco_product" : total crop production(in monetary units per ha) 
-#' \item "eco_benefit" : total crop benefits (in monetary units per ha) 
-#' \item "eco_grossmargin" : Gross Margin (benefits - costs, in monetary units per ha) 
+#' \item "eco_yield" : total crop yield (in weight or volume units per ha) 
+#' \item "eco_cost" : operational crop costs (in monetary units per ha) 
+#' \item "eco_product" : total crop products (in monetary units per ha) 
+#' \item "eco_margin" : Margin (products - operational costs, in monetary units per ha) 
 #' \item "HLIR_dynamics", "H_dynamics", "L_dynamics", "IR_dynamics", "HLI_dynamics", etc.: Epidemic dynamics
 #' related to the specified sanitary status (H, L, I or R and all their combinations). Graphics only,
 #' works only if graphic=TRUE.
@@ -118,11 +125,11 @@
 #' above which a pathogen genotype is unlikely to go extinct, used to characterise the time to invasion
 #' of resistant hosts (several values are computed if several thresholds are given in a vector).
 #' @param GLAnoDis the absolute Green Leaf Area in absence of disease (used to compute economic outputs).
-#' @param audpc100S the audpc in a fully susceptible landscape (used as reference value for graphics and video).
+#' @param audpc100S the audpc in a fully susceptible landscape (used as reference value for graphics).
 #' @param writeTXT a logical indicating if outputs must be written in text files (TRUE, default) or not (FALSE).
 #' @param graphic a logical indicating if graphics must be generated (TRUE, default) or not (FALSE).
 #' @param videoMP4 a logical indicating if a video must be generated (TRUE) or not (FALSE, default).
-#' Works only if graphic=TRUE and epid_outputs="audpc" (or epid_outputs="all").
+#' Works only if graphic=TRUE and epid_outputs="audpc_rel" (or epid_outputs="all").
 #' @param keepRawResults a logical indicating if binary files must be kept after the end of the simulation (default=FALSE).
 #' Careful, many files may be generated if keepRawResults=TRUE.
 #' @details See ?landsepi for details on the model and assumptions.  
@@ -234,7 +241,7 @@ simul_landsepi <- function(seed = 12345, time_param = list(Nyears = 20, nTSpY =
                            genes, landscape = NULL, area,
                            rotation, basic_patho_param, disp_patho, disp_host, pI0 = 5e-4,
                            epid_outputs = "all", evol_outputs = "all", thres_breakdown = 50000,
-                           GLAnoDis = 1.48315, audpc100S = 0.38,
+                           GLAnoDis = 1.48315, audpc100S = 0.76, #0.38,
                            writeTXT = TRUE, graphic = TRUE, videoMP4 = FALSE, keepRawResults = FALSE) {
 
   # Host parameters
@@ -250,7 +257,7 @@ simul_landsepi <- function(seed = 12345, time_param = list(Nyears = 20, nTSpY =
     sigmoid_plateau_host = 1,
     cultivars_genes_list = cultivars_genes_list
   )
-
+  
   # Evolution parameters
   genes_param <- list(
     name = as.character(genes$geneName),
@@ -272,7 +279,7 @@ simul_landsepi <- function(seed = 12345, time_param = list(Nyears = 20, nTSpY =
       I = as.numeric(cultivars$yield_I),
       R = as.numeric(cultivars$yield_R)
     ),
-    production_cost_perHa = as.numeric(cultivars$production_cost),
+    planting_cost_perHa = as.numeric(cultivars$planting_cost),
     market_value = as.numeric(cultivars$market_value)
   )
 
@@ -321,12 +328,13 @@ simul_landsepi <- function(seed = 12345, time_param = list(Nyears = 20, nTSpY =
     ## Limits for graphics
     ylim_param <- list(
       audpc = c(0, audpc100S),
-      gla_abs = c(0, GLAnoDis),
+      audpc_rel = c(0, 1),
+      gla = c(0, GLAnoDis),
       gla_rel = c(0, 1),