JNCC/MESO
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Commit before first release

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This commit is contained in:
Steve Pegg
2019-04-14 13:39:36 +01:00
parent 1383055d3c
commit ae7af67472
9 changed files with 123 additions and 104 deletions
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2
.hgignore
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@@ -5,3 +5,5 @@ archive
data/tmp/ data/tmp/
data/new/ data/new/
node_modules/ node_modules/
app.R.orig
tmp/

23
Parses.R
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@@ -8,7 +8,7 @@ modules::import(stats)
#Improvements needed: make the selection of first row/column of nodes programmatic #Improvements needed: make the selection of first row/column of nodes programmatic
FIRST_NODE_COL <- 3 FIRST_NODE_COL <- 3
mappings <- c("TestScenario", "Map_P_BA", "Map_BA_OP", "Map_OP_ES") mappings <- c("TestScenario", "Map_P_BA", "Map_BA_OP", "Map_OP_ES", "Legend")
nodeTypes <- c("Input.Nodes", "Internal.Nodes", "Published.Nodes") nodeTypes <- c("Input.Nodes", "Internal.Nodes", "Published.Nodes")
states <- c("impact", "confidence", "growth", "recovery", "layer") states <- c("impact", "confidence", "growth", "recovery", "layer")
refs <- c(1:length(mappings)) refs <- c(1:length(mappings))
@@ -151,9 +151,9 @@ buildGraph <- function(model, desc) {
} }
print("about to build network") print("about to build network")
print(paste0(inputText, edges)) #print(paste0(inputText, edges))
net <- model2network(paste0(inputText, edges), debug = TRUE) net <- model2network(paste0(inputText, edges), debug = FALSE)
print("network build successful") print("network build successful")
@@ -169,7 +169,7 @@ buildGraph <- function(model, desc) {
cfit <- custom.fit(net, allDists) cfit <- custom.fit(net, allDists)
cat("about to calculate sample distributions") cat("about to calculate sample distributions")
print(outNodes) #print(outNodes)
sampleDists <- cpdist(cfit, nodes = outNodes, evidence = TRUE, n = 10000, method = "lw") sampleDists <- cpdist(cfit, nodes = outNodes, evidence = TRUE, n = 10000, method = "lw")
summDists <- summary(sampleDists) summDists <- summary(sampleDists)
@@ -246,7 +246,7 @@ getValidNodes <- function(mapping, prevOutputs, prefix) {
coefs[idx,] <- as.numeric(split(mapping[1, col]))[match(c("growth", "confidence", "layer"), states)] coefs[idx,] <- as.numeric(split(mapping[1, col]))[match(c("growth", "confidence", "layer"), states)]
} }
print(coefs) #print(coefs)
return(data.frame( return(data.frame(
code = c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))), code = c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))),
@@ -263,7 +263,7 @@ getCode <- function(name, nodeDF) {
} }
getValidEdges <- function(mapping, nodeDF, prevEdge = NULL, prefix) { getValidEdges <- function(mapping, nodeDF, prevEdge = NULL, prefix) {
utils::str(nodeDF) #utils::str(nodeDF)
edgeCols <- c("inputNode", "outputNode", "impact") edgeCols <- c("inputNode", "outputNode", "impact")
edgeM <- matrix(data = NA, nrow = 0, ncol = length(edgeCols), dimnames = list(NULL, edgeCols)) edgeM <- matrix(data = NA, nrow = 0, ncol = length(edgeCols), dimnames = list(NULL, edgeCols))
@@ -338,6 +338,7 @@ parseSheet <- function(fName) {
p_ba <- parseMapping(readXL(fName,mappings[2], startRow = 1), scenario, prefix = "ba") p_ba <- parseMapping(readXL(fName,mappings[2], startRow = 1), scenario, prefix = "ba")
p_op <- parseMapping(readXL(fName,mappings[3], startRow = 1), p_ba, prefix = "op") p_op <- parseMapping(readXL(fName,mappings[3], startRow = 1), p_ba, prefix = "op")
p_es <- parseMapping(readXL(fName,mappings[4], startRow = 1), p_op, prefix = "es") p_es <- parseMapping(readXL(fName,mappings[4], startRow = 1), p_op, prefix = "es")
legend <- readXL(fName,mappings[5], startRow = 1)
#print("building graphs") #print("building graphs")
@@ -347,12 +348,10 @@ parseSheet <- function(fName) {
print("sheet load completed") print("sheet load completed")
return( return(
#list( list(
#pressBioAss = p_baNet, p_esMap = p_es,
#pressOpProc = p_opNet, legend = legend
#pressEcoServ = p_esNet, )
p_esMap = p_es
#)
) )
} else { } else {

200
app.R
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@@ -24,15 +24,6 @@ impacts <- c("Very High", ">= High", ">= Medium", ">= Low", "All")
thresholds <- c(0.97, 0.9, 0.45, 0.17, 0) thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
impLabels <- c("Very High", "High", "Medium", "Low", "Very Low") impLabels <- c("Very High", "High", "Medium", "Low", "Very Low")
legends <- c("Pressures",
"Suspension feeders",
"Mobile and burrow dwellers",
"Predators",
"Epifauna and algae",
"Functional groups",
"Output processes",
"Output enablers",
"Ecosystem services")
ui <- dashboardPage( ui <- dashboardPage(
dashboardHeader(title = "JNCC MESO online", dashboardHeader(title = "JNCC MESO online",
@@ -84,21 +75,70 @@ ui <- dashboardPage(
), ),
dashboardSidebar( dashboardSidebar(
sidebarMenu(id = "tabs", sidebarMenu(id = "tabs",
menuItem("Pressure Test", tabName = "1", icon = icon("arrow-down")), menuItem("Introduction", tabName = "1", icon = icon("arrow-down")),
menuItem("Bayesian Network", tabName = "2", icon = icon("atom")), menuItem("Pressure Test", tabName = "2", icon = icon("arrow-down")),
menuItem("Bayesian Network", tabName = "3", icon = icon("atom")),
#menuItem("Habitats", tabName = "3", icon = icon("atlas")), #menuItem("Habitats", tabName = "3", icon = icon("atlas")),
#menuItem("Ingestion", tabName = "3", icon = icon("utensils")), #menuItem("Ingestion", tabName = "3", icon = icon("utensils")),
selectInput("modelSelect", "Select MESO model", choices = c(""), selected = NULL, multiple = FALSE), selectInput("modelSelect", "Select MESO model", choices = c(""), selected = NULL, multiple = FALSE),
downloadButton("download", "", icon = icon("download")), downloadButton("download", "", icon = icon("download")),
uiOutput("pressureList") uiOutput("pressureList")
#selectInput("layerSelect", "Select Transition",
# choices = transitions,
# selected = NULL, multiple = FALSE)
) )
), ),
dashboardBody( dashboardBody(
tabItems( tabItems(
tabItem(tabName = "1", h2("Impact Distribution"), tabItem(
tabName = "1", h2("Introduction"),
tags$p(
style = "font-size: 12pt",
"This website is provided for the Joint Nature Conservation Committee (JNCC) and is provided by",
tags$a(href = "https://avsdev.uk", "AVS Developments", target = "_BLANK"),
", working under contract to ",
tags$a(href = "https://www.mba.ac.uk", "the Marine Biology Association.", target = "_BLANK")
),
tags$p(
style = "font-size: 12pt",
"This website provides a Proof of Concept visualisation tool to assist in understanding the probabilitic impact that
Anthropogenic Pressures (i.e. human activities) has on the habitats of sub-littoral areas of the United Kingdom."
),
tags$p(
style = "font-size: 12pt",
"The tool provides a mapping using a Continuous Gaussian Bayesian Belief Network from the
Anthropogenic Pressures through the biotopes and to the output processes and ultimately the
Ecosystem services, to which the habitat supports."
),
tags$p(
style = "font-size: 12pt",
"By selecting combinations of pressures on the left hand side bar, the impact on biotopes and functions of the
habitat can be estimated on the graphs shown on the Pressure test page.
The Bayesian Network page shows the structure of the Bayesian Network itself. "
),
tags$p(
style = "font-size: 12pt",
"Five substrate types have been modelled (coarse sediment, mixed sediment, mud, rock and sand)."
),
tags$p(
style = "font-size: 12pt",
"Impact of pressures are as defined in ",
tags$a(href = "https://www.marlin.ac.uk/sensitivity/sensitivity_rationale",
"the Marine Evidence based Sensitivity Assessment (MarESA).", target = "_BLANK")
),
tags$p(
style = "margin-top: 150px; font-size: 12pt",
"Further information on the rationalale and supporting information can be found in the Studiy's Final Report
available as a download from the Help pages selectable from the Question Mark logo on the
top right hand side of the website."
),
tags$p(
style = "margin-top: 150px; font-size: 10pt",
"GDPR Notice: This website only uses cookies to provide core functionality. No personal data cookies are used."
),
tags$p(
style = "font-size: 10pt",
"Copyright Notice: All images, logos and sources are property and copyright of their respected owners"
)
),
tabItem(tabName = "2", h2("Impact Distribution"),
fluidRow( fluidRow(
column( column(
width = 6, width = 6,
@@ -119,7 +159,7 @@ ui <- dashboardPage(
h4("Effect on Ecosystem services"), h4("Effect on Ecosystem services"),
plotlyOutput("layer3", height = "270px") %>% withSpinner() plotlyOutput("layer3", height = "270px") %>% withSpinner()
), ),
tabItem(tabName = "2",h2("Bayesian Network"), tabItem(tabName = "3",h2("Bayesian Network"),
fluidPage( fluidPage(
p("Graphical output of the Bayesian Network. Note: The graph will only draw if pressures are applied!"), p("Graphical output of the Bayesian Network. Note: The graph will only draw if pressures are applied!"),
fluidRow( fluidRow(
@@ -152,20 +192,7 @@ ui <- dashboardPage(
) )
) )
) )
), )
#tabItem(tabName = "3",h4("Habitats"),
# fluidPage(
# google_mapOutput(outputId = "map", width = "100%", height = "750px")
# )
#),
tabItem(tabName = "3",h4("Ingestion"),
fluidPage(
p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
fileInput("fileSelect", "Choose Excel Spreadsheet File (xlsx format)", multiple = FALSE, accept = "xlsx"),
fluidRow(renderUI("status")),
actionButton("loadAB", "Load") # icon = "upload")
)
)
) )
) )
) )
@@ -175,7 +202,6 @@ server <- function(input, output, session) {
print("Loading data") print("Loading data")
#set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
dataStorage <- "data/" dataStorage <- "data/"
models <- NULL models <- NULL
@@ -187,9 +213,6 @@ server <- function(input, output, session) {
) )
.likelihoods <- reactiveValues( .likelihoods <- reactiveValues(
p_ba = NULL,
ba_os = NULL,
os_es = NULL,
p_es = NULL p_es = NULL
) )
@@ -210,6 +233,7 @@ server <- function(input, output, session) {
.selections <- reactiveValues( .selections <- reactiveValues(
model = 1, model = 1,
#runOnce = FALSE,
bbnImpact = 1, bbnImpact = 1,
bbnNames = FALSE, bbnNames = FALSE,
bbnEdges = FALSE, bbnEdges = FALSE,
@@ -217,7 +241,6 @@ server <- function(input, output, session) {
) )
getImpact <- function(v) { getImpact <- function(v) {
print(v)
if ((v == "INS") || (v == "IV")) return(.resistanceScores[1]) if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
if ((v == "HR") || (v == "III")) return(.resistanceScores[2]) if ((v == "HR") || (v == "III")) return(.resistanceScores[2])
if ((v == "MR") || (v == "II")) return(.resistanceScores[3]) if ((v == "MR") || (v == "II")) return(.resistanceScores[3])
@@ -235,12 +258,12 @@ server <- function(input, output, session) {
for (idx in 1:length(fileList)) { for (idx in 1:length(fileList)) {
print(paste("attempting to load", paste0(dataStorage, fileList[idx]))) print(paste("attempting to load", paste0(dataStorage, fileList[idx])))
tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx])) wb <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
print(tmp) #print(tmp)
tmp$edges$values <- sapply(tmp$edges$impact, getImpact) wb$p_es$edges$values <- sapply(wb$p_es$edges$impact, getImpact)
if (!is.null(tmp)) { if (!is.null(wb)) {
modelList[[cnt]] <- tmp modelList[[cnt]] <- wb
models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5))) models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
print(paste("Model file successfully loaded", fileList[idx])) print(paste("Model file successfully loaded", fileList[idx]))
#save(tmp, file = "tmp.RData") #save(tmp, file = "tmp.RData")
@@ -259,20 +282,9 @@ server <- function(input, output, session) {
isolate({ isolate({
#if (layer == 1) layerStr = "ba" else if (layer == 2) layerStr = "op" else if (layer == 3) layerStr = "es" modelList[[.selections$model]]$p_es$edges$values <<- sapply(modelList[[.selections$model]]$p_es$edges$impact, getImpact)
modelList[[.selections$model]]$p_es$nodes$growth <<- .resistanceScores["ssgr"]
modelList[[.selections$model]]$p_es$nodes$confidence <<- .resistanceScores["pressSD"]
#layerRange <- which(startsWith(thisModel$nodes$code, layerStr))
#nodeCodes <- thisModel$nodes$code[layerRange]
#nodeNames <- thisModel$nodes$name[layerRange]
thisModel <- modelList[[.selections$model]]
modelList[[.selections$model]]$edges$values <<- sapply(thisModel$edges$impact, getImpact)
modelList[[.selections$model]]$nodes$growth <<- .resistanceScores["ssgr"]
modelList[[.selections$model]]$nodes$confidence <<- .resistanceScores["pressSD"]
thisModel <- modelList[[.selections$model]] thisModel <- modelList[[.selections$model]]
@@ -293,7 +305,7 @@ server <- function(input, output, session) {
expr <- substr(expr, 1, nchar(expr)-2) expr <- substr(expr, 1, nchar(expr)-2)
expr <- paste0(expr, ")") expr <- paste0(expr, ")")
thisNet <- parser$buildGraph(thisModel, desc = list(inputCode = "p", outputCodes = c("ba", "op", "es"))) thisNet <- parser$buildGraph(thisModel$p_es, desc = list(inputCode = "p", outputCodes = c("ba", "op", "es")))
sampleDists <- cpdist( sampleDists <- cpdist(
fitted = thisNet$cfit, fitted = thisNet$cfit,
@@ -305,19 +317,19 @@ server <- function(input, output, session) {
) )
}) })
print(sampleDists) #print(sampleDists)
#displayCols <- match(nodeCodes, colnames(sampleDists)) #displayCols <- match(nodeCodes, colnames(sampleDists))
sampleDists <- sampleDists[,match(thisModel$nodes$code, colnames(sampleDists))] sampleDists <- sampleDists[,match(thisModel$p_es$nodes$code, colnames(sampleDists))]
means <- apply(sampleDists, 2, mean) means <- apply(sampleDists, 2, mean)
stdDev <- apply(sampleDists, 2, sd) stdDev <- apply(sampleDists, 2, sd)
print(paste("Building likelihoods from model, sample dists", length(thisModel$nodes$name), length(sampleDists))) print(paste("Building likelihoods from model, sample dists", length(thisModel$p_es$nodes$name), length(sampleDists)))
return(data.frame( return(data.frame(
name = thisModel$nodes$name, name = thisModel$p_es$nodes$name,
code = thisModel$nodes$code, code = thisModel$p_es$nodes$code,
layer = thisModel$nodes$layer, layer = thisModel$p_es$nodes$layer,
range = c( range = c(
apply(sampleDists, 2, min), apply(sampleDists, 2, min),
means - 2*stdDev, means - 2*stdDev,
@@ -334,6 +346,7 @@ server <- function(input, output, session) {
observeEvent(input$modelSelect, { observeEvent(input$modelSelect, {
.selections$model <<- match(input$modelSelect, models) .selections$model <<- match(input$modelSelect, models)
#.selections$runOnce <<- TRUE
}) })
observeEvent(reactiveValuesToList(input), { observeEvent(reactiveValuesToList(input), {
@@ -348,13 +361,10 @@ server <- function(input, output, session) {
newStatus <- data.frame(code = pressures$code, status = status, stringsAsFactors = FALSE) newStatus <- data.frame(code = pressures$code, status = status, stringsAsFactors = FALSE)
if (!identical(newStatus, .selections$pressStatus)) { if (!identical(newStatus, .selections$pressStatus)) { #} || .selections$runOnce) {
#.selections$runOnce = FALSE
print("Running calc") print("Running calc")
#.likelihoods$p_ba <<- calcLikelihood(1, newStatus)
#.likelihoods$ba_os <<- calcLikelihood(2, newStatus)
#.likelihoods$os_es <<- calcLikelihood(3, newStatus)
.likelihoods$p_es <<- calcLikelihood(0, newStatus) .likelihoods$p_es <<- calcLikelihood(0, newStatus)
#write.xlsx(.likelihoods$p_es, "tmp.xlsx")
.selections$pressStatus <<- newStatus .selections$pressStatus <<- newStatus
} }
@@ -367,13 +377,19 @@ server <- function(input, output, session) {
output$pressureList <- renderUI({ output$pressureList <- renderUI({
#isolate({ #isolate({
if (!is.null(modelList[[.selections$model]]$nodes)) { if (!is.null(modelList[[.selections$model]]$p_es$nodes)) {
pressCodes <- which(startsWith(modelList[[.selections$model]]$nodes$code, "p")) pressCodes <- which(startsWith(modelList[[.selections$model]]$p_es$nodes$code, "p"))
#if (is.null(.selections$pressStatus)) status <- rep("Off", length(pressCodes)) else status <- .selections$pressStatus$status
pressures <- data.frame( pressures <- data.frame(
code = modelList[[.selections$model]]$nodes$code[pressCodes], code = modelList[[.selections$model]]$p_es$nodes$code[pressCodes],
name = modelList[[.selections$model]]$nodes$name[pressCodes], name = modelList[[.selections$model]]$p_es$nodes$name[pressCodes],
#status = status,
stringsAsFactors = FALSE stringsAsFactors = FALSE
) )
print(pressures)
#This assumes all pressures are the same...
setPressures(pressures) setPressures(pressures)
btnList <- apply(pressures, 1, makeRadioButtons) btnList <- apply(pressures, 1, makeRadioButtons)
} }
@@ -382,12 +398,10 @@ server <- function(input, output, session) {
observeEvent(input$bbnImpactSelect, { observeEvent(input$bbnImpactSelect, {
#filter nodes and edges to #filter nodes and edges to
.selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)] .selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
print(paste("Setting bbn impact", .selections$bbnImpact))
}) })
observeEvent(input$bbnDisplayNames, { observeEvent(input$bbnDisplayNames, {
.selections$bbnNames <- input$bbnDisplayNames .selections$bbnNames <- input$bbnDisplayNames
print(.selections$bbnNames)
}) })
observeEvent(input$bbnDisplayEdges, { observeEvent(input$bbnDisplayEdges, {
@@ -430,9 +444,6 @@ server <- function(input, output, session) {
.resistanceScores["pressSD"] <<- input$l1PressSD .resistanceScores["pressSD"] <<- input$l1PressSD
print("Running calc") print("Running calc")
#.likelihoods$p_ba <<- calcLikelihood(1, .selections$pressStatus)
#.likelihoods$ba_os <<- calcLikelihood(2, .selections$pressStatus)
#.likelihoods$os_es <<- calcLikelihood(3, .selections$pressStatus)
.likelihoods$p_es <<- calcLikelihood(0, .selections$pressStatus) .likelihoods$p_es <<- calcLikelihood(0, .selections$pressStatus)
removeModal() removeModal()
@@ -440,7 +451,7 @@ server <- function(input, output, session) {
output$nodeTable <- DT::renderDataTable( output$nodeTable <- DT::renderDataTable(
modelList[[.selections$model]]$nodes, modelList[[.selections$model]]$p_es$nodes,
selection = "single", selection = "single",
server = TRUE, server = TRUE,
escape = FALSE, escape = FALSE,
@@ -449,7 +460,7 @@ server <- function(input, output, session) {
) )
output$edgeTable <- DT::renderDataTable( output$edgeTable <- DT::renderDataTable(
modelList[[.selections$model]]$edges, modelList[[.selections$model]]$p_es$edges,
selection = "single", selection = "single",
server = TRUE, server = TRUE,
escape = FALSE, escape = FALSE,
@@ -464,19 +475,19 @@ server <- function(input, output, session) {
} }
makeBbnGraph <- function(model) { makeBbnGraph <- function(model) {
nodes <- model$nodes nodes <- model$p_es$nodes
if (.selections$bbnEdges) { if (.selections$bbnEdges) {
labels <- sapply(model$edges$values, getLabel) labels <- sapply(model$p_es$edges$values, getLabel)
} else { } else {
labels <- rep("", nrow(model$edges)) labels <- rep("", nrow(model$p_es$edges))
} }
edges <- data.frame( edges <- data.frame(
id = rownames(model$edges), id = rownames(model$p_es$edges),
from = match(model$edges$input, nodes$code), from = match(model$p_es$edges$input, nodes$code),
to = match(model$edges$output, nodes$code), to = match(model$p_es$edges$output, nodes$code),
values = model$edges$values, values = model$p_es$edges$values,
label = labels, label = labels,
arrows = "to", arrows = "to",
stringsAsFactors = FALSE stringsAsFactors = FALSE
@@ -489,7 +500,7 @@ server <- function(input, output, session) {
nodeSpacing <- ifelse(.selections$bbnNames, 600, 150) nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
palette <- brewer.pal(length(legends), "RdYlGn") palette <- brewer.pal(nrow(model$legend), "Set3")
nodes <- data.frame( nodes <- data.frame(
id = rownames(nodes), id = rownames(nodes),
@@ -528,13 +539,16 @@ server <- function(input, output, session) {
edgeNet <- edges edgeNet <- edges
} }
legendDF <- data.frame( legendDF <- data.frame(
id = 1:length(legends), id = 1:nrow(model$legend),
label = legends, label = model$legend,
color = palette, color = palette,
stringsAsFactors = FALSE stringsAsFactors = FALSE
) )
print(legendDF)
visNetwork(nodeNet, edgeNet, width = "100%", main = "Bayesian Belief Network", submain = input$modelSelect) %>% visNetwork(nodeNet, edgeNet, width = "100%", main = "Bayesian Belief Network", submain = input$modelSelect) %>%
visExport() %>% visExport() %>%
visLegend(useGroups = FALSE, addNodes = legendDF) %>% visLegend(useGroups = FALSE, addNodes = legendDF) %>%
@@ -556,11 +570,13 @@ server <- function(input, output, session) {
paste0("data/", input$modelSelect, ".xlsx") paste0("data/", input$modelSelect, ".xlsx")
} }
genPlot <- function(boxPlot, title) { genPlot <- function(boxPlot, title, paletteLength) {
if (nrow(boxPlot) > 0) { if (nrow(boxPlot) > 0) {
palette <- brewer.pal(length(legends), "RdYlGn") print(paste('Palette length', paletteLength))
names(palette) <- 1:length(legends)
palette <- brewer.pal(paletteLength, "Set3")
names(palette) <- 1:paletteLength
#print(paste("Box plot, colours", nrow(boxPlot), length(colours))) #print(paste("Box plot, colours", nrow(boxPlot), length(colours)))
#cat(colours) #cat(colours)
@@ -580,7 +596,9 @@ server <- function(input, output, session) {
thisPlot <- .likelihoods$p_es[inScope, c(1,3,4)] thisPlot <- .likelihoods$p_es[inScope, c(1,3,4)]
colnames(thisPlot) <- c(name, "Group", "Range") colnames(thisPlot) <- c(name, "Group", "Range")
title <- paste(input$modelSelect, name, "Box Plot") title <- paste(input$modelSelect, name, "Box Plot")
genPlot(thisPlot, title) paletteLength <- nrow(modelList[[.selections$model]]$legend)
print(paste('prep plot palette', paletteLength))
genPlot(thisPlot, title, paletteLength)
} }
} }

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